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+bibtex citation key,DOI version of key,bibtex citation
+okubo2024integrated,10.1145/3651890.3672226,"@inproceedings{okubo2024integrated,
+author = {Okubo, Ryu and Jacobs, Luke and Wang, Jinhua and Bowers, Steven and Soltanaghai, Elahe},
+title = {Integrated Two-way Radar Backscatter Communication and Sensing with Low-power IoT Tags},
+year = {2024},
+isbn = {9798400706141},
+publisher = {Association for Computing Machinery},
+address = {New York, NY, USA},
+url = {https://doi.org/10.1145/3651890.3672226},
+doi = {10.1145/3651890.3672226},
+abstract = {Integrated Sensing and Communication (ISAC) represents an innovative paradigm for enhancing spectrum and hardware utilization for both sensing and communication. A specific type of ISAC, radar backscatter communication, involves low-power nodes embedding data onto radar signal reflections rather than generating new signals. However, existing radar backscatter techniques only facilitate uplink communication from the tag to the radar, neglecting downlink communication. This paper introduces BiScatter, an integrated radar backscatter communication and sensing system that enables simultaneous uplink and downlink backscatter communication, radar sensing, and backscatter localization. This is achieved through the design of chirp-slope-shift-keying modulation on top of Frequency Modulated Continuous Wave (FMCW) radars, complemented by passive differential circuitry at the backscatter tags for low-power decoding. BiScatter also presents a packet structure compatible with off-the-shelf radars that offer accurate data processing and synchronization between radar and tag. We prototype this backscatter network in both 9GHz and 24GHz, demonstrating its capability to extend across different frequency bands. Our evaluations demonstrate that BiScatter supports two-way backscatter communication with BER lower than 10-3 up to 7m range and centimeter-level tag localization accuracy on top of off-the-shelf FMCW radars. The presented approach significantly augments the versatility and efficiency of ISAC for low-power devices.},
+booktitle = {Proceedings of the ACM SIGCOMM 2024 Conference},
+pages = {327–339},
+numpages = {13},
+keywords = {backscatter communication, integrated sensing and communication, mmWave backscattering, two-way communication, radar sensing},
+location = {Sydney, NSW, Australia},
+series = {ACM SIGCOMM '24}
+}"
+ye2024dissecting,10.1145/3651890.3672250,"@inproceedings{ye2024dissecting,
+author = {Ye, Wei and Hu, Xinyue and Sleder, Steven and Zhang, Anlan and Dayalan, Udhaya Kumar and Hassan, Ahmad and Fezeu, Rostand A. K. and Jajoo, Akshay and Lee, Myungjin and Ramadan, Eman and Qian, Feng and Zhang, Zhi-Li},
+title = {Dissecting Carrier Aggregation in 5G Networks: Measurement, QoE Implications and Prediction},
+year = {2024},
+isbn = {9798400706141},
+publisher = {Association for Computing Machinery},
+address = {New York, NY, USA},
+url = {https://doi.org/10.1145/3651890.3672250},
+doi = {10.1145/3651890.3672250},
+abstract = {By aggregating multiple channels, Carrier Aggregation (CA) is an important technology for boosting cellular network bandwidth. Given diverse radio bands made available in 5G networks, CA plays a particularly critical role in achieving the goal of multi-Gbps throughput performance. In this paper, we carry out a timely comprehensive measurement study of CA  in commercial 5G networks (as well as 4G networks). We identify the key factors that influence whether CA is deployed and when, as well as which band combinations are used. Thus, we reveal the challenges posed by CA in 5G performance analysis and prediction as well as their implications in application quality-of-experience (QoE). We argue for and develop a novel CA-aware deep learning framework, dubbed Prism5G, which explicitly accounts for the complexity introduced by CA to more effectively predict 5G network throughput performance. Through extensive evaluations, we demonstrate the superiority of Prism5G over existing throughput prediction algorithms. Prism5G improves 5G throughput prediction accuracy by over 14\% on average and a maximum of 22\%. Using two use cases as examples, we further illustrate how Prism5G can aid applications in optimizing QoE performance.},
+booktitle = {Proceedings of the ACM SIGCOMM 2024 Conference},
+pages = {340–357},
+numpages = {18},
+keywords = {carrier aggregation, 4G, 5G, network measurement, mobile network throughput prediction, deep learning},
+location = {Sydney, NSW, Australia},
+series = {ACM SIGCOMM '24}
+}"
+chen2024soda,10.1145/3651890.3672260,"@inproceedings{chen2024soda,
+author = {Chen, Tianyu and Lin, Yiheng and Christianson, Nicolas and Akhtar, Zahaib and Dharmaji, Sharath and Hajiesmaili, Mohammad and Wierman, Adam and Sitaraman, Ramesh K.},
+title = {SODA: An Adaptive Bitrate Controller for Consistent High-Quality Video Streaming},
+year = {2024},
+isbn = {9798400706141},
+publisher = {Association for Computing Machinery},
+address = {New York, NY, USA},
+url = {https://doi.org/10.1145/3651890.3672260},
+doi = {10.1145/3651890.3672260},
+abstract = {The primary objective of adaptive bitrate (ABR) streaming is to enhance users' quality of experience (QoE) by dynamically adjusting the video bitrate in response to changing network conditions. However, users often find frequent bitrate switching frustrating due to the resulting inconsistency in visual quality over time, especially during live streaming when buffer lengths are short. In this paper, we propose a practical smoothness optimized dynamic adaptive (SODA) controller that specifically addresses this problem while remaining deployable. SODA is backed by theoretical guarantees and has shown superior performance in empirical evaluations. Specifically, our numerical simulations show a 9.55\% to 27.8\% QoE improvement and our prototype evaluation shows a 30.4\% QoE improvement compared to the state-of-the-art baselines. In order to be widely deployable, SODA performs bitrate horizon planning in polynomial time compared to brute force approaches that suffer from exponential complexity. To demonstrate its real-world practicality, we deployed SODA on a wide range of devices within the production network of Amazon Prime Video. Production experiments show that SODA reduced bitrate switching by up to 88.8\% and increased average stream viewing duration by up to 5.91\% compared to a fine-tuned production baseline.},
+booktitle = {Proceedings of the ACM SIGCOMM 2024 Conference},
+pages = {613–644},
+numpages = {32},
+keywords = {adaptive bitrate streaming, smoothed online convex optimization},
+location = {Sydney, NSW, Australia},
+series = {ACM SIGCOMM '24}
+}"
+johnson2024dauth,10.1145/3651890.3672263,"@inproceedings{johnson2024dauth,
+author = {Johnson, Matthew and Singanamalla, Sudheesh and Durand, Nick and Jang, Esther Han Boel and Sevilla, Spencer and Heimerl, Kurtis},
+title = {dAuth: A Resilient Authentication Architecture for Federated Private Cellular Networks},
+year = {2024},
+isbn = {9798400706141},
+publisher = {Association for Computing Machinery},
+address = {New York, NY, USA},
+url = {https://doi.org/10.1145/3651890.3672263},
+doi = {10.1145/3651890.3672263},
+abstract = {We present dAuth, an approach to device authentication in private cellular networks which refactors the responsibilities of authentication to enable multiple small private cellular networks to federate together to provide a more reliable and resilient service than could be achieved on their own. dAuth is designed to be backwards compatible with off-the-shelf 4G and 5G cellular devices and can be incrementally deployed today. It uses cryptographic secret sharing and a division of concerns between sensitive data stored with backup networks and non-sensitive public directory data to securely scale authentication across multiple redundant nodes operating among different and untrusted organizations. Specifically, it allows a collection of pre-configured backup networks to authenticate users on behalf of their home network while the home network is unavailable. We evaluate dAuth's performance with production equipment from an active federated community network, finding that it is able to work with existing systems. We follow this with an evaluation using a simulated 5G RAN and find that it performs comparably to a standalone cloud-based 5G core at low load, and outperforms a centralized core at high load due to its innate load-sharing properties.},
+booktitle = {Proceedings of the ACM SIGCOMM 2024 Conference},
+pages = {373–391},
+numpages = {19},
+keywords = {LTE, 5G, authentication, cellular networks, secret sharing, community networks},
+location = {Sydney, NSW, Australia},
+series = {ACM SIGCOMM '24}
+}"
+k2024unveiling,10.1145/3651890.3672269,"@inproceedings{k2024unveiling,
+author = {K. Fezeu, Rostand A. and Fiandrino, Claudio and Ramadan, Eman and Carpenter, Jason and de Freitas, Lilian Coelho and Bilal, Faaiq and Ye, Wei and Widmer, Joerg and Qian, Feng and Zhang, Zhi-Li},
+title = {Unveiling the 5G Mid-Band Landscape: From Network  to Performance and Application QoE},
+year = {2024},
+isbn = {9798400706141},
+publisher = {Association for Computing Machinery},
+address = {New York, NY, USA},
+url = {https://doi.org/10.1145/3651890.3672269},
+doi = {10.1145/3651890.3672269},
+abstract = {5G in mid-bands has become the dominant  of choice in the world. We present - to the best of our knowledge - the first comprehensive and comparative cross-country measurement study of commercial mid-band 5G s in Europe and the U.S., filling a gap in the existing 5G measurement studies. We unveil the key 5G mid-band channels and configuration parameters used by various operators in these countries, and identify the major factors that impact the observed 5G performance both from the network (physical layer) perspective as well as the application perspective. We characterize and compare 5G mid-band throughput and latency performance by dissecting the 5G configurations, lower-layer parameters as well as  settings. By cross-correlating 5G parameters with the application decision process, we demonstrate how 5G parameters affect application QoE metrics and suggest a simple approach for QoE enhancement. Our study sheds light on how to better configure and optimize 5G mid-band networks, and provides guidance to users and application developers on operator choices and application QoE tuning. We released the datasets and artifacts at https://github.com/SIGCOMM24-5GinMidBands/artifacts.},
+booktitle = {Proceedings of the ACM SIGCOMM 2024 Conference},
+pages = {358–372},
+numpages = {15},
+keywords = {5G, 5G mid-band, 5G mmWave, mid-band vs. mmWave, PHY layer, measurement, latency, video streaming, performance, QoE, dataset},
+location = {Sydney, NSW, Australia},
+series = {ACM SIGCOMM '24}
+}"
+sun2024multi,10.1145/3672196.3673394,"@inproceedings{sun2024multi,
+author = {Sun, Yuan-Chun and Shi, Yuang and Ooi, Wei Tsang and Huang, Chun-Ying and Hsu, Cheng-Hsin},
+title = {Multi-frame Bitrate Allocation of Dynamic 3D Gaussian Splatting Streaming Over Dynamic Networks},
+year = {2024},
+isbn = {9798400707117},
+publisher = {Association for Computing Machinery},
+address = {New York, NY, USA},
+url = {https://doi.org/10.1145/3672196.3673394},
+doi = {10.1145/3672196.3673394},
+abstract = {Dynamic 3D Gaussian splats have emerged as an exciting new data type for modeling interactive photo-realistic 3D scenes. This work considers the problem of bitrate allocation for streaming dynamic 3D Gaussian splats under dynamic network conditions. We model four parameters that influence the rate-distortion trade-offs for different attribute categories and propose an efficient Model-driven Gradient Ascent (MGA) algorithm to search for the optimal parameters that achieve high visual quality while keeping the bitrate below a given threshold across multiple frames. In our experiments, MGA achieves up to 5.46 dB in PSNR improvement over the baseline. We further proposed an adaptive MGA that reduces close to 3x computational time with negligible visual quality loss.},
+booktitle = {Proceedings of the 2024 SIGCOMM Workshop on Emerging Multimedia Systems},
+pages = {1–7},
+numpages = {7},
+keywords = {3D Gaussian Splatting, Adaptive streaming, Bitrate allocation, Computer graphics, System design},
+location = {Sydney, NSW, Australia},
+series = {EMS '24}
+}"
+asim2024impact,10.1145/3672196.3673395,"@inproceedings{asim2024impact,
+author = {Asim, Rohail and Subramanian, Lakshmi and Zaki, Yasir},
+title = {Impact of Congestion Control on Mixed Reality Applications},
+year = {2024},
+isbn = {9798400707117},
+publisher = {Association for Computing Machinery},
+address = {New York, NY, USA},
+url = {https://doi.org/10.1145/3672196.3673395},
+doi = {10.1145/3672196.3673395},
+abstract = {The rapid increase in popularity of Virtual Reality (VR) and Augmented Reality (AR) has paved the way for the development of new applications that have the potential to revolutionize the current landscape of industries such as entertainment, education, and healthcare. A core component required to enable the development of these prospective applications is the ability to stream immersive videos in high quality with ultra-low latency. As a significant percentage of VR video traffic is expected to be delivered over mobile networks, it is important to evaluate if these networks are capable of supporting immersive video streaming. Although next-generation mobile networks offer the ultra-high bandwidth capabilities required to support AR/VR applications, it is currently unclear if current Congestion Control Algorithms (CCAs) are capable of effectively utilizing these networks to meet the strict throughput and latency requirements demanded by these applications. This paper aims to evaluate the performance of existing CCAs for such AR/VR applications. We study the performance of five prominent CCAs to evaluate: (i) the performance of these CCAs in 3G, 4G, and 5G environments for streaming current VR videos; and (ii) the performance of these CCAs in simulations with the expected bandwidth requirements of future AR/VR applications.},
+booktitle = {Proceedings of the 2024 SIGCOMM Workshop on Emerging Multimedia Systems},
+pages = {21–26},
+numpages = {6},
+keywords = {Congestion Control, Measurement, Mixed Reality},
+location = {Sydney, NSW, Australia},
+series = {EMS '24}
+}"
+li2024eloquent,10.1145/3672198.3673797,"@inproceedings{10.1145/3672198.3673797,
+author = {Li, Hanchen and Liu, Yuhan and Cheng, Yihua and Ray, Siddhant and Du, Kuntai and Jiang, Junchen},
+title = {Eloquent: A More Robust Transmission Scheme for LLM Token Streaming},
+year = {2024},
+isbn = {9798400707131},
+publisher = {Association for Computing Machinery},
+address = {New York, NY, USA},
+url = {https://doi.org/10.1145/3672198.3673797},
+doi = {10.1145/3672198.3673797},
+abstract = {To render each generated token in real-time for users, the Large Language Model (LLM) server generates tokens one by one and streams each token (or group of a few tokens) through the network to the user right after generation, which we refer to as LLM token streaming. However, under unstable network conditions, the LLM token streaming experience could suffer greatly from stalls since one packet loss could block the rendering of later tokens even if the packets containing them arrive on time. With a measurement study, we show that current applications suffer from increased stalls under unstable networks.For this emerging token streaming problem in LLM Chatbots that differs from previous multimedia and text applications, we propose a novel transmission scheme, called Eloquent, which puts newly generated tokens as well as currently unacknowledged tokens in the next outgoing packet. This ensures that each packet contains some new tokens and, in the meantime, is independently rendered when received, avoiding the aforementioned stalls caused by missing packets. Through simulation under various networks, we show Eloquent reduces stall ratio (proportion of token rendering wait time) by 71.0\% compared to the retransmission method commonly used by real chatbot applications and by 31.6\% compared to the baseline packet duplication scheme. By tailoring Eloquent to fit the token-by-token generation of LLM, we enable the Chatbots to respond like an eloquent speaker for users to better enjoy pervasive AI.},
+booktitle = {Proceedings of the 2024 SIGCOMM Workshop on Networks for AI Computing},
+pages = {34–40},
+numpages = {7},
+keywords = {Large Language Models, Real-Time Communication, Token Streaming},
+location = {Sydney, NSW, Australia},
+series = {NAIC '24}
+}"
+consul2024reliable,10.1145/3672200.3673877,"@inproceedings{10.1145/3672200.3673877,
+author = {Consul, Prakhar and Joshi, Neeraj and Budhiraja, Ishan and Biswas, Sujit and Kumar, Neeraj and Sharma, Sachin and Abraham, Ajith},
+title = {A Reliable Zero-Trust Network for Task Offloading in Vehicular Systems Using an Asynchronous Federated Learning Approach in 6G},
+year = {2024},
+isbn = {9798400707155},
+publisher = {Association for Computing Machinery},
+address = {New York, NY, USA},
+url = {https://doi.org/10.1145/3672200.3673877},
+doi = {10.1145/3672200.3673877},
+abstract = {In the emerging 6G era, vehicles are extensively connected to wireless networks through edge-accessible roadside units (RSUs). The increasing number of connected vehicles and vehicle services introduces a significant security challenge known as the ""zero-trust network (ZTN)."" This necessitates a shift from traditional methods of resource slicing and scheduling. This study focuses on ensuring reliable 6G vehicular services, particularly addressing the scenario of task offloading between vehicles, which involves managing communication resources. We propose a method that uses a logical model to assign an edge node score (ENS) to evaluate the security of edge nodes, thereby protecting vehicles from potential threats posed by untrusted edge access points. Vehicles select edge nodes with high ENS scores for task offloading. Also, we used a federated asynchronous reinforcement learning approach to enhance the management of offloaded tasks. Simulation results show that the proposed approach effectively organizes the resources and ensures the security of vehicle data.},
+booktitle = {Proceedings of the SIGCOMM Workshop on Zero Trust Architecture for Next Generation Communications},
+pages = {25–30},
+numpages = {6},
+keywords = {6G, Asynchronous Federated learning, Edge Node Score, Edge Vehicular Network, Resource slicing, Zero-trust Network},
+location = {Sydney, NSW, Australia},
+series = {ZTA-NextGen '24}
+}"
+lu2023millimeter,10.1145/3603269.3604873,"@inproceedings{lu2023millimeter,
+  title={A millimeter wave backscatter network for two-way communication and localization},
+  author={Lu, Haofan and Mazaheri, Mohammad and Rezvani, Reza and Abari, Omid},
+  booktitle={Proceedings of the ACM SIGCOMM 2023 Conference},
+  pages={49--61},
+  year={2023}
+}"
+rajiullah2023carl,10.1145/3609382.3610510,"@inproceedings{rajiullah2023carl,
+  title={CARL-W: a testbed for empirical analyses of 5g and starlink performance},
+  author={Rajiullah, Mohammad and Caso, Giuseppe and Brunstrom, Anna and Karlsson, Jonas and Alfredsson, Stefan and Alay, Ozgu},
+  booktitle={Proceedings of the 3rd ACM Workshop on 5G and Beyond Network Measurements, Modeling, and Use Cases},
+  pages={1--7},
+  year={2023}
+}"
+broner20235g,10.1145/3609382.3610511,"@inproceedings{broner20235g,
+  title={5G-MANTRA: Multi-Access Network Testbed for Research on ATSSS},
+  author={Broner, Matan and Lee, Sangwoo and Jin, Liuyi and Stoleru, Radu},
+  booktitle={Proceedings of the 3rd ACM Workshop on 5G and Beyond Network Measurements, Modeling, and Use Cases},
+  pages={8--13},
+  year={2023}
+}"
+almasan2023enhancing,10.1145/3609382.3610509,"@inproceedings{almasan2023enhancing,
+  title={Enhancing 5g radio planning with graph representations and deep learning},
+  author={Almasan, Paul and Su{\'a}rez-Varela, Jos{\'e} and Lutu, Andra and Cabellos-Aparicio, Albert and Barlet-Ros, Pere},
+  booktitle={Proceedings of the 3rd ACM Workshop on 5G and Beyond Network Measurements, Modeling, and Use Cases},
+  pages={14--20},
+  year={2023}
+}"
+caloyannis2023software,10.1145/3609382.3610512,"@inproceedings{caloyannis2023software,
+  title={Software Defined Radio platform to evaluate processing latency of 5G NR MIMO functions},
+  author={Caloyannis, Karen and Vergne, Ana{\""\i}s and Martins, Philippe},
+  booktitle={Proceedings of the 3rd ACM Workshop on 5G and Beyond Network Measurements, Modeling, and Use Cases},
+  pages={21--27},
+  year={2023}
+}"
+ni2023cellfusion,10.1145/3603269.3604832,"@inproceedings{ni2023cellfusion,
+  title={Cellfusion: Multipath vehicle-to-cloud video streaming with network coding in the wild},
+  author={Ni, Yunzhe and Zheng, Zhilong and Lin, Xianshang and Gao, Fengyu and Zeng, Xuan and Liu, Yirui and Xu, Tao and Wang, Hua and Zhang, Zhidong and Du, Senlang and others},
+  booktitle={Proceedings of the ACM SIGCOMM 2023 Conference},
+  pages={668--683},
+  year={2023}
+}"
+dhawaskar2023converge,10.1145/3603269.3604822,"@inproceedings{dhawaskar2023converge,
+  title={Converge: Qoe-driven multipath video conferencing over webrtc},
+  author={Dhawaskar Sathyanarayana, Sandesh and Lee, Kyunghan and Grunwald, Dirk and Ha, Sangtae},
+  booktitle={Proceedings of the ACM SIGCOMM 2023 Conference},
+  pages={637--653},
+  year={2023}
+}"
+ghabashneh2023dragonfly,10.1145/3603269.3604876,"@inproceedings{ghabashneh2023dragonfly,
+  title={Dragonfly: Higher perceptual quality for continuous 360 video playback},
+  author={Ghabashneh, Ehab and Bothra, Chandan and Govindan, Ramesh and Ortega, Antonio and Rao, Sanjay},
+  booktitle={Proceedings of the ACM SIGCOMM 2023 Conference},
+  pages={516--532},
+  year={2023}
+}"
+eid2023enabling,10.1145/3603269.3604814,"@inproceedings{eid2023enabling,@inproceedings{eid2023enabling,
+  title={Enabling long-range underwater backscatter via van atta acoustic networks},
+  author={Eid, Aline and Rademacher, Jack and Akbar, Waleed and Wang, Purui and Allam, Ahmed and Adib, Fadel},
+  booktitle={Proceedings of the ACM SIGCOMM 2023 Conference},
+  pages={1--19},
+  year={2023}
+}"
+wu2023enabling,10.1145/3603269.3604817,"@inproceedings{wu2023enabling,
+  title={Enabling ubiquitous WiFi sensing with beamforming reports},
+  author={Wu, Chenhao and Huang, Xuan and Huang, Jun and Xing, Guoliang},
+  booktitle={Proceedings of the ACM SIGCOMM 2023 Conference},
+  pages={20--32},
+  year={2023}
+}"
+lazarev2023resilient,10.1145/3603269.3604841,"@inproceedings{lazarev2023resilient,
+  title={Resilient baseband processing in virtualized rans with slingshot},
+  author={Lazarev, Nikita and Ji, Tao and Kalia, Anuj and Kim, Daehyeok and Marinos, Ilias and Yan, Francis Y and Delimitrou, Christina and Zhang, Zhiru and Akella, Aditya},
+  booktitle={Proceedings of the ACM SIGCOMM 2023 Conference},
+  pages={654--667},
+  year={2023}
+}"
+wang2023towards,10.1145/3603269.3604881,"@inproceedings{wang2023towards,
+  title={Towards practical and scalable molecular networks},
+  author={Wang, Jiaming and {\""O}{\u{g}}{\""u}t, Sevda and Al Hassanieh, Haitham and Krishnaswamy, Bhuvana},
+  booktitle={Proceedings of the ACM SIGCOMM 2023 Conference},
+  pages={62--76},
+  year={2023}
+}"
+chen2023underwater,10.1145/3603269.3604851,"@inproceedings{chen2023underwater,
+  title={Underwater 3D positioning on smart devices},
+  author={Chen, Tuochao and Chan, Justin and Gollakota, Shyamnath},
+  booktitle={Proceedings of the ACM SIGCOMM 2023 Conference},
+  pages={33--48},
+  year={2023}
+}"
+liu2023mobile,10.1145/3609395.3610593,"@inproceedings{liu2023mobile,
+  title={Mobile volumetric video streaming system through implicit neural representation},
+  author={Liu, Junhua and Wang, Yuanyuan and Wang, Yan and Wang, Yufeng and Cui, Shuguang and Wang, Fangxin},
+  booktitle={Proceedings of the 2023 Workshop on Emerging Multimedia Systems},
+  pages={1--7},
+  year={2023}
+}"
+yu2023rtcsr,10.1145/3609395.3610601,"@inproceedings{yu2023rtcsr,
+  title={RTCSR: Zero-latency Aware Super-resolution for WebRTC Mobile Video Streaming},
+  author={Yu, Qian and Li, Qing and He, Rui and Shi, Wanxin and Jiang, Yong},
+  booktitle={Proceedings of the 2023 Workshop on Emerging Multimedia Systems},
+  pages={54--59},
+  year={2023}
+}"
+guo2023power, 10.1145/3609395.3610598,"@inproceedings{guo2023power,
+  title={The Power of Asynchronous SLAM in Multi-User AR over Cellular Networks: A Measurement Study},
+  author={Guo, Yuting and Wang, Sizhe and Ghoshal, Moinak and Hu, Y Charlie and Koutsonikolas, Dimitrios},
+  booktitle={Proceedings of the 2023 Workshop on Emerging Multimedia Systems},
+  pages={34--40},
+  year={2023}
+}"
+pasandi2023improving,10.1145/3609389.3610570,"@inproceedings{barahouei2023improving,
+  title={Improving ble fingerprint radio maps: A method based on fuzzy clustering and weighted interpolation},
+  author={Barahouei Pasandi, Hannaneh and Moradbeikie, Azin and Barros, Daniel and Verde, David and Paiva, Sara and Lopes, Sergio Ivan},
+  booktitle={Proceedings of the 1st Workshop on Enhanced Network Techniques and Technologies for the Industrial IoT to Cloud Continuum},
+  pages={41--47},
+  year={2023}
+}"
+despres2023detagtive,10.1145/3609396.3610544,"@inproceedings{despres2023detagtive,
+  title={DeTagTive: Linking MACs to protect against malicious BLE trackers},
+  author={Despres, Tess and Davis, Noelle and Dutta, Prabal and Wagner, David},
+  booktitle={Proceedings of the Second Workshop on Situating Network Infrastructure with People, Practices, and Beyond},
+  pages={1--7},
+  year={2023}
+}"
+wei2022knew,10.1145/3522783.3529526,"@inproceedings{wei2022knew,
+  title={KNEW: Key generation using neural networks from wireless channels},
+  author={Wei, Xue and Saha, Dola},
+  booktitle={Proceedings of the 2022 ACM workshop on wireless security and machine learning},
+  pages={45--50},
+  year={2022}
+}"
+chen2022underwater,10.1145/3544216.3544258,"@inproceedings{chen2022underwater,@inproceedings{chen2022underwater,
+  title={Underwater messaging using mobile devices},
+  author={Chen, Tuochao and Chan, Justin and Gollakota, Shyamnath},
+  booktitle={Proceedings of the ACM SIGCOMM 2022 Conference},
+  pages={545--559},
+  year={2022}
+}"
+hassan2022vivisecting,10.1145/3544216.3544217,"title={Underwater messaging using mobile devices},@inproceedings{hassan2022vivisecting,
+  title={Vivisecting mobility management in 5G cellular networks},
+  author={Hassan, Ahmad and Narayanan, Arvind and Zhang, Anlan and Ye, Wei and Zhu, Ruiyang and Jin, Shuowei and Carpenter, Jason and Mao, Z Morley and Qian, Feng and Zhang, Zhi-Li},
+  booktitle={Proceedings of the ACM SIGCOMM 2022 Conference},
+  pages={86--100},
+  year={2022}
+}"
+yuan2022understanding,10.1145/3544216.3544219,"author={Chen, Tuochao and Chan, Justin and Gollakota, Shyamnath},@inproceedings{yuan2022understanding,
+  title={Understanding 5G performance for real-world services: A content provider's perspective},
+  author={Yuan, Xinjie and Wu, Mingzhou and Wang, Zhi and Zhu, Yifei and Ma, Ming and Guo, Junjian and Zhang, Zhi-Li and Zhu, Wenwu},
+  booktitle={Proceedings of the ACM SIGCOMM 2022 Conference},
+  pages={101--113},
+  year={2022}
+}"
+yang2022mobile,10.1145/3544216.3544237,"@inproceedings{yang2022mobile,
+  title={Mobile access bandwidth in practice: Measurement, analysis, and implications},
+  author={Yang, Xinlei and Lin, Hao and Li, Zhenhua and Qian, Feng and Li, Xingyao and He, Zhiming and Wu, Xudong and Wang, Xianlong and Liu, Yunhao and Liao, Zhi and others},
+  booktitle={Proceedings of the ACM SIGCOMM 2022 Conference},
+  pages={114--128},
+  year={2022}
+}"
+zhao2022seed,10.1145/3544216.3544260,"@inproceedings{zhao2022seed,
+  title={Seed: a sim-based solution to 5g failures},
+  author={Zhao, Jinghao and Tan, Zhaowei and Xu, Yifei and Zhang, Zhehui and Lu, Songwu},
+  booktitle={Proceedings of the ACM SIGCOMM 2022 Conference},
+  pages={129--142},
+  year={2022}
+}"
+meng2022achieving,10.1145/3544216.3544225,"@inproceedings{meng2022achieving,
+  title={Achieving consistent low latency for wireless real-time communications with the shortest control loop},
+  author={Meng, Zili and Guo, Yaning and Sun, Chen and Wang, Bo and Sherry, Justine and Liu, Hongqiang Harry and Xu, Mingwei},
+  booktitle={Proceedings of the ACM SIGCOMM 2022 Conference},
+  pages={193--206},
+  year={2022}
+}"
+uyeda2022sdn,10.1145/3544216.3544231,"@inproceedings{uyeda2022sdn,
+  title={SDN in the stratosphere: Loon's aerospace mesh network},
+  author={Uyeda, Frank and Alvidrez, Marc and Kline, Erik and Petrini, Bryce and Barritt, Brian and Mandle, David and Alexander, Aswin Chandy},
+  booktitle={Proceedings of the ACM SIGCOMM 2022 Conference},
+  pages={264--280},
+  year={2022}
+}"
+li2022case,10.1145/3544216.3544231,"@inproceedings{li2022case,
+  title={A case for stateless mobile core network functions in space},
+  author={Li, Yuanjie and Li, Hewu and Liu, Wei and Liu, Lixin and Chen, Yimei and Wu, Jianping and Wu, Qian and Liu, Jun and Lai, Zeqi},
+  booktitle={Proceedings of the ACM SIGCOMM 2022 Conference},
+  pages={298--313},
+  year={2022}
+}"
+gong2022empowering,10.1145/3544216.3544270,"@inproceedings{gong2022empowering,
+  title={Empowering smart buildings with self-sensing concrete for structural health monitoring},
+  author={Gong, Zheng and Han, Lubing and An, Zhenlin and Yang, Lei and Ding, Siqi and Xiang, Yu},
+  booktitle={Proceedings of the ACM SIGCOMM 2022 Conference},
+  pages={560--575},
+  year={2022}
+}"
+oppermann2022higher,10.1145/3544216.3544261,"@inproceedings{oppermann2022higher,
+  title={Higher-order modulation for acoustic backscatter communication in metals},
+  author={Oppermann, Peter and Renner, Christian},
+  booktitle={Proceedings of the ACM SIGCOMM 2022 Conference},
+  pages={576--587},
+  year={2022}
+}"
+shenoy2022rf,10.1145/3544216.3544256,"@inproceedings{shenoy2022rf,
+  title={Rf-protect: privacy against device-free human tracking},
+  author={Shenoy, Jayanth and Liu, Zikun and Tao, Bill and Kabelac, Zachary and Vasisht, Deepak},
+  booktitle={Proceedings of the ACM SIGCOMM 2022 Conference},
+  pages={588--600},
+  year={2022}
+}"
+gupta2022cyclops,10.1145/3544216.3544255,"@inproceedings{gupta2022cyclops,
+  title={Cyclops: An fso-based wireless link for vr headsets},
+  author={Gupta, Himanshu and Curran, Max and Longtin, Jon and Rockwell, Torin and Zheng, Kai and Dasari, Mallesham},
+  booktitle={Proceedings of the ACM SIGCOMM 2022 Conference},
+  pages={601--614},
+  year={2022}
+}"
+ghoshal2022depth,10.1145/3538394.3546042,"@inproceedings{ghoshal2022depth,
+  title={An in-depth study of uplink performance of 5G mmWave networks},
+  author={Ghoshal, Moinak and Kong, Z Jonny and Xu, Qiang and Lu, Zixiao and Aggarwal, Shivang and Khan, Imran and Li, Yuanjie and Hu, Y Charlie and Koutsonikolas, Dimitrios},
+  booktitle={Proceedings of the ACM SIGCOMM Workshop on 5G and Beyond Network Measurements, Modeling, and Use Cases},
+  pages={29--35},
+  year={2022}
+}"
+kousias2022implications,10.1145/3538394.3546041,"@inproceedings{kousias2022implications,
+  title={Implications of handover events in commercial 5G non-standalone deployments in Rome},
+  author={Kousias, Konstantinos and Rajiullah, Mohammad and Caso, Giuseppe and Alay, Ozgu and Brunstrom, Anna and De Nardis, Luca and Neri, Marco and Ali, Usman and Di Benedetto, Maria-Gabriella},
+  booktitle={Proceedings of the ACM SIGCOMM Workshop on 5G and Beyond Network Measurements, Modeling, and Use Cases},
+  pages={22--27},
+  year={2022}
+}"
+haile2022performance,10.1145/3538394.3546040,"@inproceedings{haile2022performance,
+  title={Performance of QUIC congestion control algorithms in 5G networks},
+  author={Haile, Habtegebreil and Grinnemo, Karl-Johan and Ferlin, Simone and Hurtig, Per and Brunstrom, Anna},
+  booktitle={Proceedings of the ACM SIGCOMM Workshop on 5G and Beyond Network Measurements, Modeling, and Use Cases},
+  pages={15--21},
+  year={2022}
+}"
+rao2022prediction,10.1145/3538394.3546039,"@inproceedings{rao2022prediction,
+  title={Prediction and exposure of delays from a base station perspective in 5G and beyond networks},
+  author={Rao, Akhila and T{\""a}rneberg, William and Fitzgerald, Emma and Corneo, Lorenzo and Zavodovski, Aleksandr and Rai, Omkar and Johansson, Sixten and Berggren, Viktor and Riaz, Hassam and Kilinc, Caner and others},
+  booktitle={Proceedings of the ACM SIGCOMM Workshop on 5G and Beyond Network Measurements, Modeling, and Use Cases},
+  pages={8--14},
+  year={2022}
+}"
+wei20225gperf,10.1145/3538394.3546044,"@inproceedings{wei20225gperf,
+  title={5GPerf: profiling open source 5G RAN components under different architectural deployments},
+  author={Wei, Cuidi and Kak, Ahan and Choi, Nakjung and Wood, Timothy},
+  booktitle={Proceedings of the ACM SIGCOMM Workshop on 5G and Beyond Network Measurements, Modeling, and Use Cases},
+  pages={43--49},
+  year={2022}
+}"
+gholami2022application,10.1145/3538401.3546598,"@inproceedings{gholami2022application,
+  title={Application-specific, dynamic reservation of 5G compute and network resources by using reinforcement learning},
+  author={Gholami, Anousheh and Rao, Kunal and Hsiung, Wang-Pin and Po, Oliver and Sankaradas, Murugan and Baras, John S and Chakradhar, Srimat},
+  booktitle={Proceedings of the ACM SIGCOMM Workshop on Network-Application Integration},
+  pages={19--25},
+  year={2022}
+}"
+mori2022preliminary,10.1145/3538393.3544932,"@inproceedings{mori2022preliminary,
+  title={A preliminary analysis of data collection and retrieval scheme for green information-centric wireless sensor networks},
+  author={Mori, Shintaro},
+  booktitle={Proceedings of the ACM SIGCOMM Workshop on Networked Sensing Systems for a Sustainable Society},
+  pages={1--6},
+  year={2022}
+}"
+an2025tooth,n/a,"@inproceedings{an2025tooth,
+  title={Tooth: Toward Optimal Balance of Video $\{$QoE$\}$ and Redundancy Cost by $\{$Fine-Grained$\}$$\{$FEC$\}$ in Cloud Gaming Streaming},
+  author={An, Congkai and Zhang, Huanhuan and Wang, Shibo and Kang, Jingyang and Zhou, Anfu and Liu, Liang and Ma, Huadong and Meng, Zili and Ma, Delei and Dong, Yusheng and others},
+  booktitle={22nd USENIX Symposium on Networked Systems Design and Implementation (NSDI 25)},
+  pages={635--651},
+  year={2025}
+}"
+sentosa2025cellreplay,n/a,"@inproceedings{sentosa2025cellreplay,
+  title={$\{$CellReplay$\}$: Towards accurate record-and-replay for cellular networks},
+  author={Sentosa, William and Chandrasekaran, Balakrishnan and Godfrey, P Brighten and Hassanieh, Haitham},
+  booktitle={22nd USENIX Symposium on Networked Systems Design and Implementation (NSDI 25)},
+  pages={1169--1186},
+  year={2025}
+}"
+garg2025large,n/a,"@inproceedings{garg2025large,
+  title={Large Network $\{$UWB$\}$ Localization: Algorithms and Implementation},
+  author={Garg, Nakul and Shahid, Irtaza and Sheshadri, Ramanujan K and Sundaresan, Karthikeyan and Roy, Nirupam},
+  booktitle={22nd USENIX Symposium on Networked Systems Design and Implementation (NSDI 25)},
+  pages={1187--1203},
+  year={2025}
+}"
+kalia2025towards,n/a,"@inproceedings{kalia2025towards,
+  title={Towards Energy Efficient 5G vRAN Servers},
+  author={Kalia, Anuj and Lazarev, Nikita and Xue, Leyang and Foukas, Xenofon and Radunovic, Bozidar and Yan, Francis Y},
+  booktitle={USENIX Symposium on Networked Systems Design and Implementation (NSDI)},
+  year={2025}
+}"
+xie2025building,n/a,"@inproceedings{xie2025building,
+  title={Building Massive $\{$MIMO$\}$ Baseband Processing on a $\{$Single-Node$\}$ Supercomputer},
+  author={Xie, Xincheng and Hou, Wentao and Guo, Zerui and Liu, Ming},
+  booktitle={22nd USENIX Symposium on Networked Systems Design and Implementation (NSDI 25)},
+  pages={1221--1242},
+  year={2025}
+}"
+zhang2024tecc,n/a,"@inproceedings{zhang2024tecc,
+  title={$\{$TECC$\}$: Towards Efficient $\{$QUIC$\}$ Tunneling via Collaborative Transmission Control},
+  author={Zhang, Jiaxing and Yang, Furong and Liu, Ting and Wu, Qinghua and Zhao, Wu and Zhang, Yuanbo and Chen, Wentao and Liu, Yanmei and Guo, Hongyu and Ma, Yunfei and others},
+  booktitle={21st USENIX Symposium on Networked Systems Design and Implementation (NSDI 24)},
+  pages={253--266},
+  year={2024}
+}"
+wang2024nn,10.5555/3691825.3691868,"@inproceedings{wang2024nn,
+  title={$\{$NN-Defined$\}$ Modulator: Reconfigurable and Portable Software Modulator on $\{$IoT$\}$ Gateways},
+  author={Wang, Jiazhao and Jiang, Wenchao and Liu, Ruofeng and Hu, Bin and Gao, Demin and Wang, Shuai},
+  booktitle={21st USENIX Symposium on Networked Systems Design and Implementation (NSDI 24)},
+  pages={775--789},
+  year={2024}
+}"
+liu2024democratizing,10.5555/3691825.3691869,"@article{liu2024democratizing,
+  title={Democratizing direct-to-cell low earth orbit satellite networks},
+  author={Liu, Lixin and Li, Yuanjie and Li, Hewu and Yang, Jiabo and Liu, Wei and Lan, Jingyi and Wang, Yufeng and Li, Jiarui and Wu, Jianping and Wu, Qian and others},
+  journal={GetMobile: Mobile Computing and Communications},
+  volume={28},
+  number={2},
+  pages={5--10},
+  year={2024},
+  publisher={ACM New York, NY, USA}
+}"
+singh2024spectrumize,10.5555/3691825.3691871,"@inproceedings{singh2024spectrumize,
+  title={Spectrumize: Spectrum-efficient satellite networks for the internet of things},
+  author={Singh, Vaibhav and Chakraborty, Tusher and Jog, Suraj and Chabra, Om and Vasisht, Deepak and Chandra, Ranveer},
+  booktitle={21st USENIX Symposium on Networked Systems Design and Implementation (NSDI 24)},
+  pages={825--840},
+  year={2024}
+}"
+balasingam2024application,10.5555/3691825.3691872,"@misc{balasingam2024application,
+  title={Application-Level Service Assurance with 5G RAN Slicing. In 2024 Networked Systems Design and Implementation. USENIX},
+  author={Balasingam, Arjun and Kotaru, Manikanta and Bahl, Victor},
+  year={2024}"
+du2024orthcatter,10.5555/3691825.3691897,"@inproceedings{du2024orthcatter,
+  title={Orthcatter: High-throughput in-band $\{$OFDM$\}$ backscatter with $\{$Over-the-Air$\}$ code division},
+  author={Du, Caihui and Yu, Jihong and Zhang, Rongrong and Ren, Ju and An, Jianping},
+  booktitle={21st USENIX Symposium on Networked Systems Design and Implementation (NSDI 24)},
+  pages={1301--1314},
+  year={2024}"
+ko2023edgeric,10.5555/3691825.3691898,"@inproceedings{ko2024edgeric,
+  title={$\{$EdgeRIC$\}$: Empowering real-time intelligent optimization and control in $\{$NextG$\}$ cellular networks},
+  author={Ko, Woo-Hyun and Ghosh, Ushasi and Dinesha, Ujwal and Wu, Raini and Shakkottai, Srinivas and Bharadia, Dinesh},
+  booktitle={21st USENIX Symposium on Networked Systems Design and Implementation (NSDI 24)},
+  pages={1315--1330},
+  year={2024}
+}"
+yin2024adr,10.5555/3691825.3691899,"@inproceedings{yin2024adr,
+  title={$\{$ADR-X$\}$:$\{$ANN-Assisted$\}$ Wireless Link Rate Adaptation for $\{$Compute-Constrained$\}$ Embedded Gaming Devices},
+  author={Yin, Hao and Ramanujam, Murali and Schaefer, Joe and Adermann, Stan and Narlanka, Srihari and Lea, Perry and Netravali, Ravi and Chintalapudi, Krishna},
+  booktitle={21st USENIX Symposium on Networked Systems Design and Implementation (NSDI 24)},
+  pages={1331--1349},
+  year={2024}
+}"
+dai2024rfid+,10.5555/3691825.3691900,"@inproceedings{dai2024rfid+,
+  title={$\{$RFID+$\}$: Spatially controllable identification of $\{$UHF$\}$$\{$RFIDs$\}$ via controlled magnetic fields},
+  author={Dai, Donghui and An, Zhenlin and Gong, Zheng and Pan, Qingrui and Yang, Lei},
+  booktitle={21st USENIX Symposium on Networked Systems Design and Implementation (NSDI 24)},
+  pages={1351--1367},
+  year={2024}
+}"
+wang2024smuff,10.5555/3691825.3691901,"@inproceedings{wang2024smuff,
+  title={$\{$SMUFF$\}$: Towards Line Rate $\{$Wi-Fi$\}$ Direct Transport with Orchestrated On-device Buffer Management},
+  author={Wang, Chengke and Wang, Hao and Zhou, Yuhan and Ni, Yunzhe and Qian, Feng and Xu, Chenren},
+  booktitle={21st USENIX Symposium on Networked Systems Design and Implementation (NSDI 24)},
+  pages={1369--1383},
+  year={2024}
+}"
+kim2024nr,10.5555/3691825.3691915,"@inproceedings{kim2024nr,
+  title={NR-Surface: NextG-ready $\mu$W-reconfigurable mmWave Metasurface.},
+  author={Kim, Minseok and Ahn, Namjo and Kim, Song Min},
+  booktitle={NSDI},
+  year={2024}
+}"
+li2024cyclops,10.5555/3691825.3691916,"@inproceedings{li2024cyclops,
+  title={Cyclops: A Nanomaterial-based,$\{$Battery-Free$\}$ Intraocular Pressure ($\{$$\{$$\{$$\{$$\{$IOP$\}$$\}$$\}$$\}$$\}$) Monitoring System inside Contact Lens},
+  author={Li, Liyao and Shang, Bozhao and Wu, Yun and Xiong, Jie and Chen, Xiaojiang and Xie, Yaxiong},
+  booktitle={21st USENIX Symposium on Networked Systems Design and Implementation (NSDI 24)},
+  pages={1659--1675},
+  year={2024}
+}"
+zhang2024habitus,10.5555/3691825.3691917,"@inproceedings{zhang2024habitus,
+  title={Habitus: boosting mobile immersive content delivery through full-body pose tracking and multipath networking},
+  author={Zhang, Anlan and Wang, Chendong and Hu, Yuming and Hassan, Ahmad and Zhang, Zejun and Han, Bo and Qian, Feng and Xu, Shichang},
+  booktitle={21st USENIX Symposium on Networked Systems Design and Implementation (NSDI 24)},
+  pages={1677--1695},
+  year={2024}
+}"
+yi2024bfmsense,10.5555/3691825.3691918,"@inproceedings{yi2024bfmsense,
+  title={$\{$BFMSense$\}$:$\{$WiFi$\}$ sensing using beamforming feedback matrix},
+  author={Yi, Enze and Wu, Dan and Xiong, Jie and Zhang, Fusang and Niu, Kai and Li, Wenwei and Zhang, Daqing},
+  booktitle={21st USENIX Symposium on Networked Systems Design and Implementation (NSDI 24)},
+  pages={1697--1712},
+  year={2024}
+}"
+chae2024mmcomb,10.5555/3691825.369191,"@inproceedings{chae2024mmcomb,
+  title={$\{$mmComb$\}$: High-speed $\{$mmWave$\}$ Commodity $\{$WiFi$\}$ Backscatter},
+  author={Chae, Yoon and Lin, Zhenzhe and Bae, Kang Min and Kim, Song Min and Pathak, Parth},
+  booktitle={21st USENIX Symposium on Networked Systems Design and Implementation (NSDI 24)},
+  pages={1713--1729},
+  year={2024}
+}"
+yuan2024sidekick,10.5555/3691825.3691924,"@inproceedings{yuan2024sidekick,
+  title={Sidekick:$\{$In-Network$\}$ Assistance for Secure $\{$End-to-End$\}$ Transport Protocols},
+  author={Yuan, Gina and Sotoudeh, Matthew and Zhang, David K and Welzl, Michael and Mazi{\`e}res, David and Winstein, Keith},
+  booktitle={21st USENIX Symposium on Networked Systems Design and Implementation (NSDI 24)},
+  pages={1813--1830},
+  year={2024}
+}"
+carver2023catch,10.1145/3570361.3614081,"@inproceedings{carver2023catch,
+  title={Catch me if you can: Demonstrating laser tethering with highly mobile targets},
+  author={Carver, Charles J and Schwartz, Hadleigh and Shao, Qijia and Shade, Nicholas and Lazzaro, Joseph P and Wang, Xiaoxin and Liu, Jifeng and Fossum, Eric R and Zhou, Xia},
+  booktitle={Proceedings of the 29th Annual International Conference on Mobile Computing and Networking},
+  pages={1--3},
+  year={2023}
+}"
+fan2024passengers,10.5555/3691825.3691928,"@inproceedings{fan2024passengers,
+  title={Passengers' Safety Matters: Experiences of Deploying a $\{$Large-Scale$\}$ Indoor Delivery Monitoring System},
+  author={Fan, Xiubin and Lin, Zhongming and Hu, Yuming and Jiang, Tianrui and Qian, Feng and Yin, Zhimeng and Chan, S-H Gary and Wu, Dapeng},
+  booktitle={21st USENIX Symposium on Networked Systems Design and Implementation (NSDI 24)},
+  pages={1883--1900},
+  year={2024}
+}"
+zhou2024augur,10.5555/3691825.3691929,"@inproceedings{zhou2024augur,
+  title={$\{$AUGUR$\}$: Practical Mobile Multipath Transport Service for Low Tail Latency in $\{$Real-Time$\}$ Streaming},
+  author={Zhou, Yuhan and Wang, Tingfeng and Wang, Liying and Wen, Nian and Han, Rui and Wang, Jing and Wu, Chenglei and Chen, Jiafeng and Jiang, Longwei and Wang, Shibo and others},
+  booktitle={21st USENIX Symposium on Networked Systems Design and Implementation (NSDI 24)},
+  pages={1901--1916},
+  year={2024}
+}"
+shahid2024cloud,10.5555/3691825.3691932,"@inproceedings{shahid2024cloud,
+  title={$\{$Cloud-LoRa$\}$: Enabling Cloud Radio Access $\{$LoRa$\}$ Networks Using Reinforcement Learning Based $\{$Bandwidth-Adaptive$\}$ Compression},
+  author={Shahid, Muhammad Osama and Koch, Daniel and Raghuram, Jayaram and Krishnaswamy, Bhuvana and Chintalapudi, Krishna and Banerjee, Suman},
+  booktitle={21st USENIX Symposium on Networked Systems Design and Implementation (NSDI 24)},
+  pages={1959--1976},
+  year={2024}
+}"
+kludze2023leakyscatter,n/a,"@inproceedings{kludze2023leakyscatter,
+  title={$\{$LeakyScatter$\}$: A $\{$Frequency-Agile$\}$ directional backscatter network above 100 $\{$GHz$\}$},
+  author={Kludze, Atsutse and Ghasempour, Yasaman},
+  booktitle={20th USENIX Symposium on Networked Systems Design and Implementation (NSDI 23)},
+  pages={375--388},
+  year={2023}
+}"
+li2023rf,n/a,"@inproceedings{li2023rf,
+  title={$\{$RF-Bouncer$\}$: A programmable dual-band metasurface for sub-6 wireless networks},
+  author={Li, Xinyi and Feng, Chao and Wang, Xiaojing and Zhang, Yangfan and Xie, Yaxiong and Chen, Xiaojiang},
+  booktitle={20th USENIX Symposium on Networked Systems Design and Implementation (NSDI 23)},
+  pages={389--404},
+  year={2023}
+}"
+sentosa2023dchannel,10.1145/3446382.3448357,"@inproceedings{sentosa2023dchannel,
+  title={$\{$DChannel$\}$: Accelerating Mobile Applications With Parallel High-bandwidth and Low-latency Channels},
+  author={Sentosa, William and Chandrasekaran, Balakrishnan and Godfrey, P Brighten and Hassanieh, Haitham and Maggs, Bruce},
+  booktitle={20th USENIX Symposium on Networked Systems Design and Implementation (NSDI 23)},
+  pages={419--436},
+  year={2023}
+}"
+mishra2023openlora,n/a,"@inproceedings{mishra2023openlora,
+  title={$\{$OpenLoRa$\}$: Validating $\{$LoRa$\}$ Implementations through an Extensible and Open-sourced Framework},
+  author={Mishra, Manan and Koch, Daniel and Shahid, Muhammad Osama and Krishnaswamy, Bhuvana and Chintalapudi, Krishna and Banerjee, Suman},
+  booktitle={20th USENIX Symposium on Networked Systems Design and Implementation (NSDI 23)},
+  pages={1165--1183},
+  year={2023}"
+zhang2023vecare,n/a,"@inproceedings{zhang2023vecare,
+  title={$\{$VeCare$\}$: Statistical acoustic sensing for automotive $\{$In-Cabin$\}$ monitoring},
+  author={Zhang, Yi and Hou, Weiying and Yang, Zheng and Wu, Chenshu},
+  booktitle={20th USENIX Symposium on Networked Systems Design and Implementation (NSDI 23)},
+  pages={1185--1200},
+  year={2023}
+}"
+zhao2023slimwifi,n/a,"@inproceedings{zhao2023slimwifi,
+  title={$\{$SlimWiFi$\}$:$\{$Ultra-Low-Power$\}$$\{$IoT$\}$ Radio Architecture Enabled by Asymmetric Communication},
+  author={Zhao, Renjie and Wang, Kejia and Zheng, Kai and Zhang, Xinyu and Leung, Vincent},
+  booktitle={20th USENIX Symposium on Networked Systems Design and Implementation (NSDI 23)},
+  pages={1201--1219},
+  year={2023}
+}"
+yang2023slnet,n/a,"@inproceedings{yang2023slnet,
+  title={$\{$SLNet$\}$: A spectrogram learning neural network for deep wireless sensing},
+  author={Yang, Zheng and Zhang, Yi and Qian, Kun and Wu, Chenshu},
+  booktitle={20th USENIX Symposium on Networked Systems Design and Implementation (NSDI 23)},
+  pages={1221--1236},
+  year={2023}
+}"
+ni2023polycorn,n/a,"@inproceedings{ni2023polycorn,
+  title={$\{$POLYCORN$\}$: Data-driven cross-layer multipath networking for high-speed railway through composable schedulerlets},
+  author={Ni, Yunzhe and Qian, Feng and Liu, Taide and Cheng, Yihua and Ma, Zhiyao and Wang, Jing and Wang, Zhongfeng and Huang, Gang and Liu, Xuanzhe and Xu, Chenren},
+  booktitle={20th USENIX Symposium on Networked Systems Design and Implementation (NSDI 23)},
+  pages={1325--1340},
+  year={2023}
+}"
+boroushaki2023augmenting,n/a,"@inproceedings{boroushaki2023augmenting,
+  title={Augmenting augmented reality with $\{$Non-Line-of-Sight$\}$ perception},
+  author={Boroushaki, Tara and Lam, Maisy and Dodds, Laura and Eid, Aline and Adib, Fadel},
+  booktitle={20th USENIX Symposium on Networked Systems Design and Implementation (NSDI 23)},
+  pages={1341--1358},
+  year={2023}
+}"
+zhang2023acoustic,n/a,"@inproceedings{zhang2023acoustic,
+  title={Acoustic sensing and communication using metasurface},
+  author={Zhang, Yongzhao and Wang, Yezhou and Yang, Lanqing and Wang, Mei and Chen, Yi-Chao and Qiu, Lili and Liu, Yihong and Xue, Guangtao and Yu, Jiadi},
+  booktitle={20th USENIX Symposium on Networked Systems Design and Implementation (NSDI 23)},
+  pages={1359--1374},
+  year={2023}
+}"
+meng2023enabling,n/a,"@inproceedings{meng2023enabling,
+  title={Enabling high quality $\{$Real-Time$\}$ communications with adaptive $\{$Frame-Rate$\}$},
+  author={Meng, Zili and Wang, Tingfeng and Shen, Yixin and Wang, Bo and Xu, Mingwei and Han, Rui and Liu, Honghao and Arun, Venkat and Hu, Hongxin and Wei, Xue},
+  booktitle={20th USENIX Symposium on Networked Systems Design and Implementation (NSDI 23)},
+  pages={1429--1450},
+  year={2023}
+}"
+tan2023celldam,n/a,"@inproceedings{tan2023celldam,
+  title={$\{$CellDAM$\}$:$\{$User-Space$\}$, Rootless Detection and Mitigation for 5G Data Plane},
+  author={Tan, Zhaowei and Zhao, Jinghao and Ding, Boyan and Lu, Songwu},
+  booktitle={20th USENIX Symposium on Networked Systems Design and Implementation (NSDI 23)},
+  pages={1601--1619},
+  year={2023}
+}"
+cho2023mmwall,n/a,"@inproceedings{cho2023mmwall,
+  title={$\{$mmWall$\}$: A Steerable, Transflective Metamaterial Surface for $\{$NextG$\}$$\{$mmWave$\}$ Networks},
+  author={Cho, Kun Woo and Mazaheri, Mohammad H and Gummeson, Jeremy and Abari, Omid and Jamieson, Kyle},
+  booktitle={20th USENIX Symposium on Networked Systems Design and Implementation (NSDI 23)},
+  pages={1647--1665},
+  year={2023}
+}"
+chen2023channel,n/a,"@inproceedings{chen2023channel,
+  title={$\{$Channel-Aware$\}$ 5g $\{$RAN$\}$ slicing with customizable schedulers},
+  author={Chen, Yongzhou and Yao, Ruihao and Hassanieh, Haitham and Mittal, Radhika},
+  booktitle={20th USENIX Symposium on Networked Systems Design and Implementation (NSDI 23)},
+  pages={1767--1782},
+  year={2023}
+}"
+liu2023exploring,n/a,"@inproceedings{liu2023exploring,
+  title={Exploring practical vulnerabilities of machine learning-based wireless systems},
+  author={Liu, Zikun and Xu, Changming and Sie, Emerson and Singh, Gagandeep and Vasisht, Deepak},
+  booktitle={20th USENIX Symposium on Networked Systems Design and Implementation (NSDI 23)},
+  pages={1801--1817},
+  year={2023}
+}"
+liang2023rf,n/a,"@inproceedings{liang2023rf,
+  title={$\{$RF-Chord$\}$: Towards deployable $\{$RFID$\}$ localization system for logistic networks},
+  author={Liang, Bo and Wang, Purui and Zhao, Renjie and Guo, Heyu and Zhang, Pengyu and Guo, Junchen and Zhu, Shunmin and Liu, Hongqiang Harry and Zhang, Xinyu and Xu, Chenren},
+  booktitle={20th USENIX Symposium on Networked Systems Design and Implementation (NSDI 23)},
+  pages={1783--1799},
+  year={2023}
+}"
+basit2025metamorphosis,n/a,"@inproceedings{basit2025metamorphosis,
+  author = {Basit, Omar and Khan, Imran and Ghoshal, Moinak and Hu, Y. Charlie and Koutsonikolas, Dimitrios},
+  title = {{5G} Metamorphosis: A Longitudinal Study of {5G} Performance from the Beginning},
+  booktitle = {Proceedings of the 2025 ACM Internet Measurement Conference (IMC '25)},
+  year = {2025},
+  address = {New York, NY, USA}
+}"
+sundberg2024measuring,10.1145/3646547.3688438,"@inproceedings{sundberg2024measuring,
+  title={Measuring Network Latency from a Wireless ISP: Variations Within and Across Subnets},
+  author={Sundberg, Simon and Brunstrom, Anna and Ferlin-Reiter, Simone and H{\o}iland-J{\o}rgensen, Toke and Chac{\'o}n, Robert},
+  booktitle={Proceedings of the 2024 ACM on Internet Measurement Conference},
+  pages={29--43},
+  year={2024}
+}"
+sharma2024longitudinal,10.1145/3646547.3689007,"@inproceedings{sharma2024longitudinal,
+  title={A Longitudinal Study of the Prevalence of WiFi Bottlenecks in Home Access Networks},
+  author={Sharma, Ranya and Feamster, Nick and Richardson, Marc},
+  booktitle={Proceedings of the 2024 ACM on Internet Measurement Conference},
+  pages={44--50},
+  year={2024}
+}"
+yang2024characterizing,10.1145/3646547.3688433,"@inproceedings{yang2024characterizing,
+  title={Characterizing the Security Facets of IoT Device Setup},
+  author={Yang, Han and Kuzniar, Carson and Jiang, Chengyan and Nikolaidis, Ioanis and Haque, Israat},
+  booktitle={Proceedings of the 2024 ACM on Internet Measurement Conference},
+  pages={612--621},
+  year={2024}
+}"
+kalntis2024through,10.1145/3646547.3688452,"@inproceedings{kalntis2024through,
+  title={Through the Telco Lens: A Countrywide Empirical Study of Cellular Handovers},
+  author={Kalntis, Michail and Su{\'a}rez-Varela, Jos{\'e} and Iglesias, Jes{\'u}s Oma{\~n}a and Bhattacharjee, Anup Kiran and Iosifidis, George and Kuipers, Fernando A and Lutu, Andra},
+  booktitle={Proceedings of the 2024 ACM on Internet Measurement Conference},
+  pages={51--67},
+  year={2024}
+}"
+ibrahim2023tag,10.1145/3618257.3624834,"@inproceedings{ibrahim2023tag,
+  title={I tag, you tag, everybody tags!},
+  author={Ibrahim, Hazem and Asim, Rohail and Varvello, Matteo and Zaki, Yasir},
+  booktitle={Proceedings of the 2023 ACM on Internet Measurement Conference},
+  pages={561--568},
+  year={2023}
+}"
+bakirtzis2023characterizing,10.1145/3618257.3624807,"@inproceedings{bakirtzis2023characterizing,
+  title={Characterizing mobile service demands at indoor cellular networks},
+  author={Bakirtzis, Stefanos and Zanella, Andr{\'e} Felipe and Rubrichi, Stefania and Ziemlicki, Cezary and Smoreda, Zbigniew and Wassell, Ian and Zhang, Jie and Fiore, Marco},
+  booktitle={Proceedings of the 2023 ACM on Internet Measurement Conference},
+  pages={645--659},
+  year={2023}
+}"
+meng2023modeling,10.1145/3618257.3624808,"@inproceedings{meng2023modeling,
+  title={Modeling and generating control-plane traffic for cellular networks},
+  author={Meng, Jiayi and Huang, Jingqi and Hu, Y Charlie and Koral, Yaron and Lin, Xiaojun and Shahbaz, Muhammad and Sharma, Abhigyan},
+  booktitle={Proceedings of the 2023 ACM on Internet Measurement Conference},
+  pages={660--677},
+  year={2023}
+}"
+ghoshal2023performance,10.1145/3618257.3624814,"@inproceedings{ghoshal2023performance,
+  title={Performance of cellular networks on the wheels},
+  author={Ghoshal, Moinak and Khan, Imran and Kong, Z Jonny and Dinh, Phuc and Meng, Jiayi and Hu, Y Charlie and Koutsonikolas, Dimitrios},
+  booktitle={Proceedings of the 2023 ACM on Internet Measurement Conference},
+  pages={678--695},
+  year={2023}
+}"
+zanella2023characterizing,10.1145/3618257.3624825,"@inproceedings{zanella2023characterizing,
+  title={Characterizing and modeling session-level mobile traffic demands from large-scale measurements},
+  author={Zanella, Andr{\'e} Felipe and Bazco-Nogueras, Antonio and Ziemlicki, Cezary and Fiore, Marco},
+  booktitle={Proceedings of the 2023 ACM on Internet Measurement Conference},
+  pages={696--709},
+  year={2023}
+}"
+michel2022first,10.1145/3517745.3561416,"@inproceedings{michel2022first,
+  title={A first look at starlink performance},
+  author={Michel, Fran{\c{c}}ois and Trevisan, Martino and Giordano, Danilo and Bonaventure, Olivier},
+  booktitle={Proceedings of the 22nd ACM Internet Measurement Conference},
+  pages={130--136},
+  year={2022}
+}"
+saidi2022deep,10.1145/3517745.3561431,"@inproceedings{saidi2022deep,
+  title={Deep dive into the IoT backend ecosystem},
+  author={Saidi, Said Jawad and Matic, Srdjan and Gasser, Oliver and Smaragdakis, Georgios and Feldmann, Anja},
+  booktitle={Proceedings of the 22nd ACM internet measurement conference},
+  pages={488--503},
+  year={2022}
+}"
+perdices2022satellite,10.1145/3517745.3561432,"@inproceedings{perdices2022satellite,
+  title={When satellite is all you have: watching the internet from 550 ms},
+  author={Perdices, Daniel and Perna, Gianluca and Trevisan, Martino and Giordano, Danilo and Mellia, Marco},
+  booktitle={Proceedings of the 22nd ACM Internet Measurement Conference},
+  pages={137--150},
+  year={2022}
+}"
+paul2022importance,10.1145/3517745.3561441,"@inproceedings{paul2022importance,
+  title={The importance of contextualization of crowdsourced active speed test measurements},
+  author={Paul, Udit and Liu, Jiamo and Gu, Mengyang and Gupta, Arpit and Belding, Elizabeth},
+  booktitle={Proceedings of the 22nd ACM Internet Measurement Conference},
+  pages={274--289},
+  year={2022}
+}"
+mahimkar2022aurora,10.1145/3517745.3561455,"@inproceedings{mahimkar2022aurora,
+  title={Aurora: conformity-based configuration recommendation to improve LTE/5G service},
+  author={Mahimkar, Ajay and Ge, Zihui and Liu, Xuan and Shaqalle, Yusef and Xiang, Yu and Yates, Jennifer and Pathak, Shomik and Reichel, Rick},
+  booktitle={Proceedings of the 22nd ACM Internet Measurement Conference},
+  pages={83--97},
+  year={2022}
+}"
+kassem2022browser,10.1145/3517745.3561457,"@inproceedings{kassem2022browser,
+  title={A browser-side view of starlink connectivity},
+  author={Kassem, Mohamed M and Raman, Aravindh and Perino, Diego and Sastry, Nishanth},
+  booktitle={Proceedings of the 22nd ACM Internet Measurement Conference},
+  pages={151--158},
+  year={2022}
+}"
+baltaci2022analyzing,10.1145/3517745.3561465,"@inproceedings{baltaci2022analyzing,
+  title={Analyzing real-time video delivery over cellular networks for remote piloting aerial vehicles},
+  author={Baltaci, Ayg{\""u}n and Cech, Hendrik and Mohan, Nitinder and Geyer, Fabien and Bajpai, Vaibhav and Ott, J{\""o}rg and Schupke, Dominic},
+  booktitle={Proceedings of the 22nd ACM internet measurement conference},
+  pages={98--112},
+  year={2022}
+}"
+raca2020beyond,10.1145/3339825.3394938,"@inproceedings{raca2020beyond,
+  title={Beyond throughput, the next generation: A 5G dataset with channel and context metrics},
+  author={Raca, Darijo and Leahy, Dylan and Sreenan, Cormac J and Quinlan, Jason J},
+  booktitle={Proceedings of the 11th ACM multimedia systems conference},
+  pages={303--308},
+  year={2020}
+}"
+raca2018beyond,10.1145/3204949.3208123,"@inproceedings{raca2018beyond,
+  title={Beyond throughput: A 4G LTE dataset with channel and context metrics},
+  author={Raca, Darijo and Quinlan, Jason J and Zahran, Ahmed H and Sreenan, Cormac J},
+  booktitle={Proceedings of the 9th ACM multimedia systems conference},
+  pages={460--465},
+  year={2018}
+}"

datasets/wireless_datasets.csv

@@ -0,0 +1,133 @@
+dataset name,bibtex citation key,OSI layer at which dataset is measured,modality(ies),Availaibility (open?),Availability Annotations,Collection environment,Number of Papers using Dataset
+BiScatter Experimental Data,okubo2024integrated,L1,"RF Measurements (Bit Error Rate (BER)), Signal-to-Noise Ratio , Tag Localization Error (in cm) ",No,,Physical Lab Testbed,1
+5G/4G Carrier Aggregation Measurement Dataset,ye2024dissecting,L1,"RF Measurements: (SS-RSRP, SS-RSRQ, SINR, CQI), RAN Signaling Data",No,https://github.com/SIGCOMM24-5G-CA/artifact - broken link,Real World Deployment,2
+5G Network Dataset,"raca2020beyond, chen2024soda, ghabashneh2023dragonfly",L4,Network Performance Metrics (Throughput traces),Yes ,as per the original dataset paper: https://dl.acm.org/doi/pdf/10.1145/3339825.3394938 (or https://github.com/Purdue-ISL/Dragonfly/tree/main/bandwidth_traces),Real World Deployment,5
+4G Network Dataset,"raca2018beyond, chen2024soda",L4,Network Performance Metrics (Throughput traces),Yes,as per the original dataset paper: https://dl.acm.org/doi/pdf/10.1145/3204949.3208123,Real World Deployment,5
+ dAuth Physical LTE Testbed Data ,johnson2024dauth,"L3, L4",Network Performance Metrics (attach time/connection latency),No,,Physical Lab Testbed,1
+ dAuth Simulated 5G RAN Data,johnson2024dauth,"L3, L4",Network Performance Metrics (connection latency),No,,Physical Lab Testbed,1
+5G Mid-Band Measurement Dataset,k2024unveiling,"L1, L2, L3","RF Measurements (RSRP, RSRQ, SINR, CQI), RAN Signaling Data (RRC control messages), resource block allocation, modulation/coding selection",Yes,"curated slices, need to contact for full logs: https://github.com/SIGCOMM24-5GinMidBands/artifacts",Real World Deployment ,3
+Lumos5G dataset ,sun2024multi,"L1, L4","abstractSignalStr, LTE RSSI, LTE RSRP, LTE RSRQ, LTE RSSNR, NR ssRSRP, NR ssRSRQ,  NR ssSINR, throughput",Yes,https://ieee-dataport.org/open-access/lumos5g-dataset ((A. Narayanan et al. “Lumos5G: Mapping and Predicting Commercial mmWave 5G Throughput.” Proc. ACM IMC 2020.),Real World Deployment,5
+Cellular Channel Capacity Traces (3G/4G/5G – 6 traces),asim2024impact,L4,"Network Performance Metrics: one-minute time-series of available‐bandwidth capacity (3G, 4G, 5G)",No,,Real World Deployment ,1
+Cellular LLM Token-Streaming Packet Traces,li2024eloquent,"L3, L4",Packet/Flow Header Traces,No,,Real World Deployment,1
+6G Vehicular Task-Offloading Simulation Dataset,consul2024reliable,"L1, L2, L3, L4","Simulated RF & Network Metrics: (V2I/V2V channel rates, RB interference, queue lengths, power draw, latency, admission-rate time-series)",No,,Simulation,1
+MilBack mmWave Backscatter Indoor Testbed Data,lu2023millimeter,L1,"RF localization signals (FMCW), baseband captures at the AP, envelope-detector power samples at the node, backscatter communication symbols using two-tone OAQFM, network performance metrics (downlink SINR, max data rate, uplink SNR, BER) ",No,,Physical Lab Testbed,1
+CARL-W 5G Baseline Measurements,rajiullah2023carl,"L1, L4","RSRP, RSRQ, SINR, Throughput, Latency",Yes,https://5g.carl.kau.se/ - grafana link to dashboards,Real World Deployment,1
+CARL-W Starlink Baseline and Metadata Measurements ,rajiullah2023carl,"L1, L4","SNR, satellite direction, satellite reachability, latency, packet loss, throughput",Yes,https://zenodo.org/records/8130936,Real World Deployment,1
+5G-MANTRA ATSSS Lab Measurements,broner20235g,L4,"RTT, bytes sent, transfer rate, throughput",No,,Physical Lab Testbed,1
+5G-MANTRA Real-World 5G Deployment Measurements,broner20235g,"L4, L7","Download time, upload time, time-to-first-byte (TTFB), throughput",No,,Physical Lab Testbed,1
+	UK MNO 4G/5G Cell-Level KPI ,almasan2023enhancing,"L2, L3","PRB utilization, uplink user throughput, downlink user throughput ",No,,Real World Deployment,1
+5G NR MIMO Latency Evaluation Data,caloyannis2023software,L1,"I/Q Samples, PSS/DMRS/PDSCH, Estimated Channel Coefficients, Processing Latency, Symbol Error Rate",No ,code is open https://github.com/free5G/free5GRA),Physical Lab Testbed,1
+CELLFUSION Vehicle-to-Cloud Streaming Data,ni2023cellfusion,"L1, L4","RSRP, SINR, Packet Loss Rate, Packet Delay, Traffic Redundancy, Avg. FPS, Stall Ratio, SSIM Score",No,,Real World Deployment,1
+CONVERGE Multipath Video Conferencing Dataset,dhawaskar2023converge,"L4, L7","Path Bandwidth/Throughput, E2E Latency/RTT, Packet Loss, FEC Overhead & Utilization, Network Traces",No,,Real World Deployment,1
+Belgian Network Throughput Traces,"ghabashneh2023dragonfly, yu2023rtcsr",L4,Network Throughput Traces (bandwidth logs collected over a wireless network - time series derived from per-interval bytes and ms (≈1 s sampling)),Yes," https://github.com/Purdue-ISL/Dragonfly/tree/main/bandwidth_traces ((J. van der Hooft, S. Petrangeli, T. Wauters, R. Huysegems, P. R. Alface, T. Bostoen, and F. De Turck. HTTP/2-Based Adaptive Streaming of HEVC Video Over 4G/LTE Networks. IEEE Communications Letters, 20(11):2177–2180, 2016.)",Real World Deployment,5
+Van Atta Acoustic Backscatter (VAB) Underwater Dataset,eid2023enabling,L1,"Acoustic Waveforms, Signal-to-Noise Ratio , Bit-Error Rate (BER)",No,,Real World Deployment,1
+City-Scale Wi-Fi Capability Measurement,wu2023enabling,L2,"802.11 MAC Layer Frames (Beacons, NDPA, QoS), Device Radio Capabilities (Vendor OUI, Standard, Band, MCS, Beamforming Support)",No,,Real World Deployment,1
+BeamSense Wi-Fi Sensing Dataset,wu2023enabling,L1,"Compressed Beamforming Reports (CBR), Reconstructed Channel State Information , Angle of Arrival (AoA), Angle of Departure (AoD), Time of Flight (ToF)",No,,Real World Deployment,1
+SignFi Dataset,wu2023enabling,L1,"Channel State Information , Generated Compressed Beamforming Reports (CBR)",No,,Real World Deployment,1
+Slingshot 5G vRAN Resilience Dataset,lazarev2023resilient,"L1, L4","Fronthaul I/Q Samples, Ping Latency (RTT), TCP/UDP Throughput, Fronthaul Inter-Packet Gap",No,,Physical Lab Testbed,1
+MoMA (Molecular Multiple Access) Dataset,wang2023towards,L1,"Molecular Concentration Signal, Channel Impulse Response (CIR), Bit Error Rate (BER), Throughput",Yes,ACM artifacts available via request,Physical Lab Testbed,1
+AquaApp Underwater Acoustic Messaging Dataset,chen2023underwater,L1,"Acoustic Waveforms, Channel Impulse Response (CIR), Time of Flight (ToF), Pairwise Distance Estimates, Signal-to-Noise Ratio ",Yes, https://github.com/uw-x/underwatergps - code open no curated dataset,Real World Deployment,1
+VOINR Volumetric Video Streaming Performance Dataset,liu2023mobile,L4,Network Throughput/Bandwidth traces,No,,Real World Deployment,1
+Norway 3G Mobile Network Traces,yu2023rtcsr,L4,Network Throughput/Bandwidth Traces,No,,Real World Deployment,1
+Multi-User AR over Cellular Networks Dataset,guo2023power,L4,"Packet Traces (tcpdump), E2E Latency, Throughput/Message Size",No,,Real World Deployment,1
+BLE Fingerprint Radio Map for Indoor Localization,pasandi2023improving,L1,Received Signal Strength Indicator (RSSI),No,,Physical Lab Testbed,1
+DeTagTive Malicious BLE Tracker Dataset,despres2023detagtive,"L1, L2","BLE Advertisement Packets, Received Signal Strength Indicator (RSSI), MAC Addresses, Advertisement Timestamps / Intervals",No,,Real World Deployment,1
+KNEW Over-the-Air USRP Wi-Fi Dataset,wei2022knew,L1,"Wireless Waveforms / I/Q Samples, Channel Frequency Response (CFR) / Channel Estimates",No,,Physical Lab Testbed,1
+KNEW Simulated Rayleigh Channel Dataset,wei2022knew,L1,"Channel Impulse Response (CIR), Channel Frequency Response (CFR) / Channel Estimates",No,,Simulation,1
+AquaApp Underwater Acoustic Messaging Dataset,chen2022underwater,"L1, L2","Acoustic Waveforms, SNR per frequency bin, Channel Frequency Response (CFR), Bit Error Rate (BER), Packet Error Rate (PER), Bitrate",Yes,https://underwatermessaging.cs.washington.edu/ - code to reproduce dataset via config ,Real World Deployment,1
+5G Mobility and Performance Drive-Test Dataset,hassan2022vivisecting,"L1, L3, L4","RSRP, RSRQ, SINR , RRC measurements, Throughput , Latency/RTT , Packet Traces (pcap) , Socket Statistics (ss), Power Consumption ",Yes,https://github.com/SIGCOMM22-5GMobility/artifact,Real World Deployment,2
+Kuaishou 5G/4G Passive Performance Dataset,yuan2022understanding,"L3, L4, L7","Download Speed, Rebuffer Proportion, Power Consumption",No,,Real World Deployment,1
+Kuaishou 5G/4G Active Traceroute Dataset,yuan2022understanding,"L3, L4","RTT, Traceroute paths (IPs and hop count)",No,,Real World Deployment,1
+UUSpeedTest Mobile Bandwidth & Diagnostics Dataset,yang2022mobile,"L1, L4","RSRP, RSRQ, RSSNR, RSSI/ASU, SINR, cell bandwidth (kHz), LTE signal strength, RSSI, throughput",Yes,https://MobileBandwidth.github.io/,Real World Deployment,1
+MobileInsight/MI-LAB Public 5G/4G Signaling Traces,zhao2022seed,L3,RRC/NAS Signaling Messages,No,,Real World Deployment,1
+SEED 5G Failure & Recovery Testbed Dataset,zhao2022seed,"L3, L4","Latency (Disruption Time, Failure Detection Time), Power Consumption ",No,,Physical Lab Testbed,1
+Large-Scale Online RTC Platform Performance Dataset,meng2022achieving,"L4, L7","RTT, Frame Delay, Frame Rate",No,,Real World Deployment,1
+Real-World WiFi and Cellular Performance Traces,meng2022achieving,L4,Throughput (Goodput),No,,Real World Deployment,1
+Zhuge WiFi AP Performance Testbed Dataset,meng2022achieving,"L4, L7","RTT, Frame Delay, Frame Rate, Throughput (Goodput)",No,,Physical Lab Testbed,1
+Loon Network Operational & Telemetry Dataset,uyeda2022sdn,"L1, L3, L4","RF throughput (predicted), RF link margin, control/data-plane reachability probes, link state transitions, link uptime (derived)",Yes,https://zenodo.org/records/6629754,Real World Deployment,1
+Geostationary Mobile Satellite Signaling Traces (Inmarsat/Tiantong),li2022case,"L1, L2, L3","Signaling Messages (RRC, MM, SM), Signaling Latency",Yes,"https://github.com/yuanjieli/SpaceCore-SIGCOMM22, dropbox link",Real World Deployment,1
+Terrestrial 5G Signaling Traces (China Unicom/Mobile/Telecom),li2022case,"L1, L2, L3","RRC, MM, SM, Latency",Yes, https://github.com/yuanjieli/SpaceCore-SIGCOMM22,Real World Deployment,2
+EcoCapsule Acoustic Backscatter Performance Dataset,gong2022empowering,L1,"Throughput, Power-up Range, SNR, BER,",No,,Physical Lab Testbed,1
+Acoustic Backscatter in Metals Performance Dataset,oppermann2022higher,L1,"Data Rate (Throughput), SNR, BER, Impedance, Channel Impulse Response, Frequency Response",No,,Physical Lab Testbed,1
+RF-Protect Spoofing Performance Dataset,shenoy2022rf,L1,"FMCW radar signal, Reference signal, Spoofed 2D trajectory, Range heatmaps, Signal phase (from raw)",Yes, https://github.com/ConnectedSystemsLab/rf-protect,Physical Lab Testbed,1
+Cyclops FSO Link Performance Dataset,gupta2022cyclops,"L1, L4","Throughput, Received Power, Latency (Tracking and Pointing)",No,,Physical Lab Testbed,1
+5G mmWave Uplink Performance Dataset,ghoshal2022depth,"L1, L2, L3, L4","Throughput, RTT, Modulation coding scheme , Beam SSB Index",Yes,"https://drive.google.com/drive/folders/1OfIfisRz168Iic6mdKmLGoJjK0hdIIFP?usp=sharing, https://github.com/NUWINS/sigcomm-5gmemu-5g-mmWave-uplink-data",Real World Deployment,1
+5G NSA Handover Performance Dataset,kousias2022implications,"L1, L2, L4","SS-RSRP, SS-RSRQ, SS-SINR, SSB Beam Index, RTT, Packet Delay Variation (PDV), Packet Loss Rate (PLR), Throughput",Yes,https://zenodo.org/records/6759808,Real World Deployment,1
+Karlstad University 5G Throughput Traces,haile2022performance,L4,Throughput tracces,No,,Physical Lab Testbed,1
+Live 5G QUIC Congestion Control Performance Dataset,haile2022performance,"L1, L4","Throughput, RTT, SINR, RSRQ",No,,Physical Lab Testbed,1
+5G mmWave Base Station Metrics and RTT Dataset,rao2022prediction,"L1, L4","RTT, RSRP (Narrow Beam), SINR, Preamble Power,Transport Block Size",No,,Physical Lab Testbed,1
+OAI 5G RAN Performance & Profiling Dataset ,wei20225gperf,"L1, L2, L4","Throughput, RTT, MCS",No,,Physical Lab Testbed,1
+RL-based 5G Resource Reservation Performance Dataset,gholami2022application,L4,"Throughput, Compute Usage (# Cores)",No,,Physical Lab Testbed,1
+Green ICWSN Performance Dataset,mori2022preliminary,L4,"Throughput, Latency, Jitter collected over a wireless network",No,,Physical Lab Testbed,1
+Well-Link Cloud Gaming QoE and FEC Performance Dataset,an2025tooth,"L4, L7","Throughput (Video Bitrate), RTT, Network Loss Rate, Stall Frequency, Frame Length",No,,Real World Deployment,1
+CellReplay Light and Heavy Workload Traces,sentosa2025cellreplay,L4,"RTT, Packet Delivery Opportunity Traces, Throughput",No,,Real World Deployment,1
+Self-generated UWB localization dataset,garg2025large,"L1, L7","Range, Azimuth Angle-of-Arrival, Elevation Angle-of-Arrival, 3D Localization Error, Orientation Error",No,,Physical Lab Testbed,1
+Self-generated UWB peer-to-peer measurement traces for simulation,garg2025large,L1,"Range, Angle-of-Arrival",No,,Simulation,1
+Self-generated LTE TTI-level traffic traces,kalia2025towards,L2,Cumulative Transport Block Size,No,,Real World Deployment,1
+Self-generated 5G vRAN performance data,kalia2025towards,"L2, L4","Throughput, Latency, Buffer Status Reports",No,,Real World Deployment,1
+Self-generated massive MIMO IQ samples,xie2025building,L1,"IQ Samples, Frame Processing Latency, Throughput ",Yes,code: https://github.com/netlab-wisconsin/MegaStation - generates dataset,Simulation,1
+Self-generated real-world mobile network traces (Cellular and WiF,zhang2024tecc,L4,Bandwidth Traces ,No,,Real World Deployment,1
+Self-generated over-the-air ZigBee performance data,wang2024nn,L2,Packet Reception Ratio (PRR),No,code: https://github.com/Repo4Sub/NSDI2024 - broken link,Physical Lab Testbed,1
+Self-generated over-the-air WiFi performance data,wang2024nn,"L2, L7","Packet Reception Ratio (PRR), Reconstructed Image Quality ",No,code: https://github.com/Repo4Sub/NSDI2024 - broken link,Physical Lab Testbed,1
+Self-generated Iridium RF link measurements,liu2024democratizing,L1,RF link measurements,No,,Real World Deployment,1
+Self-generated 3GPP NTN performance data,liu2024democratizing,"L3, L4","Signaling Message Count, Service Setup Latency, Service Resumption Latency ",No,,Physical Lab Testbed,1
+Self-generated raw SDR recordings,singh2024spectrumize,L1,"IQ Samples, Packet Detection F1 Score, Decoding Accuracy ",No,,Real World Deployment,1
+Self-generated IoT satellite signals,singh2024spectrumize,L1,"IQ Samples, Packet Detection Correlation Value, Collision Resolution Accuracy ",No,,Physical Lab Testbed,1
+5G vRAN performance data,balasingam2024application,"L1, L2, L4","Signal-to-Noise Ratio, Slice Bandwidth Allocation, Throughput ",No,,Real World Deployment,1
+Beyond Throughput: The Next Generation: A 5G Dataset with Channel and Context Metrics ,balasingam2024application,L1,SNR Traces ,No,,Physical Lab Testbed,1
+Self-generated OFDM backscatter performance data,du2024orthcatter,"L1, L4","Bit Error Rate, Throughput",No,,Physical Lab Testbed,1
+Self-generated 5G Channel Quality Indicator traces ,ko2024edgeric,L1,Channel Quality Indicator Traces,No,,Physical Lab Testbed,1
+Self-generated WiFi packet traces and channel measurements from emulated gaming sessions,yin2024adr,"L1, L2, L4","Channel State Information , Signal-to-Noise Ratio, Received Signal Strength , Packet Loss Rate, Packet Transmission Time, User-perceived Audio/Video Quality (CMOS/VMAF scores) ",No,,Physical Lab Testbed,1
+Self-generated UHF RFID signals,dai2024rfid+,L1,"IQ Samples (baseband signal), Reading Rate",No,,Physical Lab Testbed,1
+Wi-Fi Direct performance data from COTS smartphones,wang2024smuff,L4,"Throughput, Flow Completion Time (FCT), Buffer Occupancy",No,,Real World Deployment,1
+Self-generated 24GHz mmWave performance data,kim2024nr,L1,"Signal-to-Noise Ratio, Bit Error Rate (BER), Synchronization Error",No,,Physical Lab Testbed,1
+UHF RFID performance and sensing data ,li2024cyclops,"L1, L7","IQ Samples, Received Signal Strength , Reading Rate, Intraocular Pressure (IOP) Measurement Error",No,,Physical Lab Testbed,1
+Habitus Motion and Wireless Performance Dataset,zhang2024habitus,"L1, L4, L7","802.11ad/ac throughput, 802.11ad/ac RSSI, 6-DoF headset motion, 3D full-body pose",No,,Physical Lab Testbed,1
+BFMSense Experimental BFM Dataset,yi2024bfmsense,L1,Beamforming Feedback Matrix,No,,Physical Lab Testbed,1
+BFMSense WARP V3 CSI Comparison Dataset,yi2024bfmsense,L1,Channel state information,No,,Physical Lab Testbed,1
+mmComb Backscatter-Modulated 60 GHz Waveform DatasetBibTeX Citation Key,chae2024mmcomb,L1,"Raw I/Q samples, channel state information (amplitude and phase)",No,,Physical Lab Testbed,1
+Sidekick Real-World Wi-Fi/Cellular Performance Data,yuan2024sidekick,"L4, L7","Goodput, De-jitter Latency",No,,Real World Deployment,1
+Lasertag Optical Tracking and Predictive Steering Dataset,carver2023catch,L1,"Target Pixel Coordinates, Laser Steering Commands",No,,Physical Lab Testbed,1
+DeMo Indoor Delivery Monitoring Dataset,fan2024passengers,L1,RSSI,Yes,https://github.com/Starry102/DeMo,Real World Deployment,1
+Cloud-LoRa Outdoor Deployment I/Q Dataset,shahid2024cloud,L1,Raw I/Q Samples,No ,,Real World Deployment,1
+LeakyScatter Sub-THz Backscatter Link Characterization Dataset,kludze2023leakyscatter,L1,Power spectrum measurements of the backscattered THz signal,No,,Physical Lab Testbed,1
+RF-Bouncer Dual-Band Metasurface Performance Dataset,li2023rf,"L1, L4","SNR, Throughput, Beam Pattern Data (SNR v. Angle)",No,,Physical Lab Testbed,1
+DChannel 5G eMBB Performance Traces,sentosa2023dchannel,L4,"Path RTT, Throughput (Bandwidth)",No,,Real World Deployment,1
+OpenLoRa Benchmark Datasets,mishra2023openlora,L1,Raw I/Q samples,Yes,https://github.com/UW-CONNECT/OpenLora - code only,Physical Lab Testbed,2
+VECARE Statistical Acoustic Sensing Dataset,zhang2023vecare,L1,"Acoustic Channel Impulse Response, Acoustic Channel State Information",No,,Physical Lab Testbed,1
+SlimWiFi Asymmetric Communication Performance Dataset,zhao2023slimwifi,"L2, L4","Frame Error Rate, Goodput",No,,Physical Lab Testbed,1
+Widar 3.0: WiFi-based Activity Recognition Dataset,yang2023slnet,L1,"Wi-Fi Channel State Information, doppler frequenyc spectograms",Yes,https://ieee-dataport.org/open-access/widar-30-wifi-based-activity-recognition-dataset,Physical Lab Testbed,1
+HSRNet Dataset,ni2023polycorn,"L1, L4","TCP Packet Traces (Throughput, RTT), LTE Control-Plane Messages (RSRP)",Yes,https://soar.group/projects/hsrnet/dataset.html,Real World Deployment,1
+X-AR RF-Visual Localization Dataset,boroushaki2023augmenting,"L1, L7","Wideband RFID Channel Measurements, 6-DoF Head Pose (location/rotation)",No,,Physical Lab Testbed,2
+Acoustic Metasurface Beamforming Performance Dataset,zhang2023acoustic,"L1, L2","SNR, Distance Estimation Error, Angle of Arrival (AoA) Estimation Error, Bit Error Rate (BER), Frame Error Rate",No,,Physical Lab Testbed,1
+Tencent START Cloud Gaming Network Traces,"meng2023enabling, zhou2024augur","L4, L7","RTT, Throughput, Packet Loss Rate, Frame Stall Rate",No,,Real World Deployment,2
+5G Mobile Carrier Performance Dataset,tan2023celldam,"L4, L7","TCP/UDP Throughput, RTT, Frame Rate (video streaming)",No,,Real World Deployment,1
+Private 5G Testbed Performance Data,tan2023celldam,L4,"TCP/UDP Throughput, RTT",No,,Physical Lab Testbed,1
+mmWall Metamaterial Beam-Steering Performance Dataset,cho2023mmwall,L1,S-parameters (S21),No,,Physical Lab Testbed,1
+Modified 5G Cellular Traces from LTScope,chen2023channel,"L1, L4","SNR, CQI, Throughput, Flow Completion Time (FCT)",Yes, original paper (xie2020pbec),Real World Deployment,3
+Self-generated Synthetic 5G CQI Traces,chen2023channel,L1,"Synthetically-generated CQI, SNR, Throughput",No,,Simulation,1
+Self-generated Wi-Fi CSI Data for FIRE System Attack,liu2023exploring,L1,"CSI, SNR, Spectral Efficiency",No,,Physical Lab Testbed,1
+DLoc Indoor Localization Dataset ,liu2023exploring,L1,"Wi-Fi Channel State Information (amplitude + phase per subcarrier/antenna), Angle of Arrival (AoA) & Time of Flight (ToF) heatmap features",Yes,"https://wcsng.github.io/wcsng/dloc, Kaggle provides the precomputed feature tensors and site/repo has raw csi which can derive features with authors’ scripts",Physical Lab Testbed,1
+RF-CHORD Wideband RFID Channel Information Dataset,liang2023rf,L1,"Wideband RFID channel information, Per-antenna measurements",Yes,https://soar.group/projects/rfid/rfchord/,Physical Lab Testbed,1
+RF-CHORD Warehouse Operational Dataset,liang2023rf,L7,"Miss-reading rate, Cross-reading rate",No,,Real World Deployment,1
+RF-CHORD Fresh Food Delivery Store Operational Dataset,liang2023rf,L7,"Miss-reading rate, Cross-reading rate",No,,Real World Deployment,1
+Ookla Speedtest Intelligence 5G/LTE Performance Dataset (2020-2023),basit2025metamorphosis,L4,"Downlink/Uplink throughput, RTT/latency",Yes,"provided by Ookla, specific slice not linked but registry at https://registry.opendata.aws/speedtest-global-performance/",Real World Deployment,1
+JackRabbit Wireless ISP Latency and Traffic Dataset,sundberg2024measuring,"L3, L4","RTT, summary stats per subnet (min/max/mean/median/p95/count, 4 ms bins), rx/tx packet & byte counts, ECN counters",Yes, https://zenodo.org/records/13388093,Real World Deployment,1
+Netrics Home WiFi vs. ISP Throughput Dataset,sharma2024longitudinal,L4,Throughput (Internal WiFi Link). ,No,code but no data: https://github.com/internet-innovation/netrics,Real World Deployment,1
+IoT Device Setup Wireless Traffic Traces,yang2024characterizing,L2,"Raw packet captures of WiFi, and Bluetooth Low Energy (BLE) frames",No,willing to share to research labs only,Real World Deployment,1
+MNO Countrywide Cellular Handover Dataset,kalntis2024through,L3,"Handover success/failure status, Handover duration, source/target Radio Access Technology (RAT)",No ,NDA required,Real World Deployment,1
+Apple AirTag and Samsung SmartTag Location Tracking Dataset,ibrahim2023tag,"L1, L7","Reported coordinates, update interval, time-to-first-fix, location accuracy error",Yes,https://github.com/comnetsAD/Tags/tree/main,Physical Lab Testbed and Real World Deployment,1
+Countrywide Indoor Cellular Network Traffic Dataset,bakirtzis2023characterizing,L7,"Per-hour downlink bytes per indoor antenna, Per-hour uplink bytes per indoor antenna",Yes,explicitly says contact authors say processed data available (not raw) but no link,Real World Deployment,1
+US Carrier LTE Control-Plane Traffic Trace,meng2023modeling,L3,"Control-Plane Event Type, UE Identifier, Inter-arrival Time of Events",No ,generator for data open though: https://gitlab.com/serverless-5g/cellular-network-control-plane-traffgen,Real World Deployment,1
+Cellular Networks on the Wheels Driving Dataset,ghoshal2023performance,"L1, L3, L4, L7","RSRP, MCS, BLER, Handover count and duration, DL/UL Throughput, RTT (to edge server and cloud server), Application-specific QoE metrics",Yes,https://github.com/NUWiNS/imc2023-cellular-network-performance-on-wheels-data,Real World Deployment,1
+MNO Nationwide Session-Level Traffic Dataset,zanella2023characterizing,L7,"Session Arrival Rate, Session Traffic Volume, Session Duration",No ,models/code open: https://github.com/nds-group/MobileTrafficDists,Real World Deployment,1
+Self-generated Starlink LEO Satellite Performance Dataset,michel2022first,"L3, L4, L7","RTT/latency, Download throughput, Upload throughput, Packet loss rate, Traceroute hop path, Web page load time",No,link available but not loading: https://smartdata.polito.it/a-first-look-at-starlink-performance-open-data/,Real World Deployment,5
+Self-generated Geostationary SatCom Performance Dataset,michel2022first,"L3, L4, L7","RTT/latency, Download throughput, Upload throughput, Packet loss rate, Traceroute hop path, Web page load time",No,link available but not loading: https://smartdata.polito.it/a-first-look-at-starlink-performance-open-data/,Real World Deployment,4
+Passive IoT Traffic Flows from a Major European ISP,saidi2022deep,"L4, L7","Network Traffic Volume (Downstream/Upstream), Active Subscriber Line Count",No,,Real World Deployment,1
+Passive Geostationary SatCom Traffic Dataset (Europe/Africa),perdices2022satellite,"L4, L7","RTT (Satellite and Ground segments), Throughput, Traffic Volume, DNS Resolution Time",No,,Real World Deployment,1
+Ookla Speedtest Intelligence Dataset,paul2022importance,"L3, L4","Download throughput, Upload throughput, Latency/RTT, Latency-under-load (download), Latency-under-load (upload)",Yes,https://github.com/teamookla/ookla-open-data,Real World Deployment,2
+M-Lab NDT Speed Test Dataset,paul2022importance,"L3, L4","Throughput, latency/RTT, Modulation coding scheme , Beam SSB Index",Yes,https://speed.measurementlab.net/#/,Real World Deployment,2
+Operational LTE/5G Cellular Network Dataset,mahimkar2022aurora,"L1, L3, L4","RSRP, RSRQ, SINR, Throughput, Accessibility, Retainability/Call Drops, Handover Rates",No,,Real World Deployment,1
+Crowdsourced Starlink Web Performance Dataset,kassem2022browser,"L4, L7","Page Transit Time, Page Load Time, Throughput (from in-browser speed tests)",No,,Real World Deployment,1
+Starlink Active Measurement Dataset (Volunteer Nodes),kassem2022browser,"L3, L4","Throughput, Packet Loss, RTT, Hop-by-hop delay",No,,Real World Deployment,1
+UAV Real-time Video over LTE Performance Dataset ,baltaci2022analyzing,"L3, L4, L7","Handover frequency, Handover execution time, One-way latency, Packet loss rate, Video throughput (goodput), Video bitrate, Frame rate, Playback latency, Video quality (SSIM)",Yes,https://mediatum.ub.tum.de/1687221,Real World Deployment,2

papers/wireless_papers.csv

@@ -0,0 +1,108 @@
+Paper Title,Authors,Conference,Year,Datasets,Bibtex Citation Key
+Integrated Two-way Radar Backscatter Communication and Sensing with Low-power IoT Tags,"Ryu Okubo, Luke Jacobs, Jinhua Wang, Steven Bowers, and Elahe Soltanaghai",SIGCOMM,2024,BiScatter Experimental Data,okubo2024integrated
+"Dissecting Carrier Aggregation in 5G Networks: Measurement, QoE Implications and Prediction","Wei Ye, Xinyue Hu, Steven Sleder, Anlan Zhang, Udhaya Kumar Dayalan, Ahmad Hassan, Rostand A. K. Fezeu, Akshay Jajoo, Myungjin Lee, Eman Ramadan, Feng Qian, Zhi-Li Zhang",SIGCOMM,2024,5G/4G Carrier Aggregation Measurement Dataset,ye2024dissecting
+SODA: An Adaptive Bitrate Controller for Consistent High-Quality Video Streaming,"Tianyu Chen, Yiheng Lin, Nicolas Christianson, Zahaib Akhtar, Sharath Dharmaji, Mohammad Hajiesmaili, Adam Wierman, Ramesh K. Sitaraman",SIGCOMM,2024,"5G Network Dataset, 4G Network Dataset",chen2024soda
+dAuth: A Resilient Authentication Architecture for Federated Private Cellular Networks,"Matthew Johnson, Sudheesh Singanamalla, Nick Durand, Esther Jang, Spencer Sevilla, Kurtis Heimerl",SIGCOMM,2024,"dAuth Physical LTE Testbed Data, dAuth Simulated 5G RAN Data",johnson2024dauth
+Unveiling the 5G Mid-Band Landscape: From Network  to Performance and Application QoE,"Rostand A. K. Fezeu, Claudio Fiandrino, Eman Ramadan, Jason Carpenter, Lilian Coelho deFreitas, Faaiq Bilal, Wei Ye, Joerg Widmer, Feng Qian, Zhi-Li Zhang",SIGCOMM,2024,5G Mid-Band Measurement Dataset,k2024unveiling
+Multi-frame Bitrate Allocation of Dynamic 3D Gaussian Splatting Streaming Over Dynamic Networks,"Yuan-Chun Sun, Yuang Shi, Wei Tsang Ooi, Chun-Ying Huang, Cheng-Hsin Hsu",SIGCOMM,2024,Lumos5G dataset,sun2024multi
+Impact of Congestion Control on Mixed Reality Applications,Rohail Asim; Lakshmi Subramanian; Yasir Zaki,SIGCOMM,2024,"Cellular Channel Capacity Traces (3G/4G/5G – 6 traces)
+",asim2024impact
+Eloquent: A More Robust Transmission Scheme for LLM Token Streaming,Hanchen Li; Yuhan Liu; Yihua Cheng; Siddhant Ray; Kuntai Du; Junchen Jiang,SIGCOMM,2024,Cellular LLM Token-Streaming Packet Traces,li2024eloquent
+A Reliable Zero-Trust Network for Task Offloading in Vehicular Systems Using an Asynchronous Federated Learning Approach in 6G,Prakhar Consul; Neeraj Joshi; Ishan Budhiraja; Sujit Biswas; Neeraj Kumar; Sachin Sharma; Ajith Abraham,SIGCOMM,2024,6G Vehicular Task-Offloading Simulation Dataset,consul2024reliable
+A Millimeter Wave Backscatter Network for Two-Way Communication and Localization,"Haofan Lu, Mohammad Hossein Mazaheri, Reza Rezvani, Omid Abari",SIGCOMM,2023,MilBack mmWave Backscatter Indoor Testbed Data,lu2023millimeter
+CARL-W: a Testbed for Empirical Analyses of 5G and Starlink Performance,"Mohammad Rajiullah, Giuseppe Caso, Anna Brunstrom, Jonas Karlsson, Stefan Alfredsson, Ozgu Alay",SIGCOMM,2023,"CARL-W 5G Baseline Measurements, CARL-W Starlink Baseline and Metadata Measurements ",rajiullah2023carl
+5G-MANTRA: Multi-Access Network Testbed for Research on ATSSS,"Matan Broner, Sangwoo Lee, Liuyi Jin, Radu Stoleru",SIGCOMM,2023,"5G-MANTRA ATSSS Lab Measurements, 5G-MANTRA Real-World 5G Deployment Measurements",broner20235g
+Enhancing 5G Radio Planning with Graph Representations and Deep Learning,"Paul Almasan, José Suárez-Varela, Andra Lutu, Albert Cabellos-Aparicio, Pere Barlet-Ros",SIGCOMM,2023,	UK MNO 4G/5G Cell-Level KPI ,almasan2023enhancing
+Software Defined Radio platform to evaluate processing latency of 5G NR MIMO functions,"Karen Caloyannis, Anaïs Vergne, Philippe Martins ",SIGCOMM,2023,5G NR MIMO Latency Evaluation Data,caloyannis2023software
+CellFusion: Multipath Vehicle-to-Cloud Video Streaming with Network Coding in the Wild,"Yunzhe Ni, Zhilong Zheng, Xianshang Lin, Fengyu Gao, Xuan Zeng, Yirui Liu, Tao Xu, Hua Wang, Zhidong Zhang, Senlang Du, Guang Yang, Yuanchao Su, Dennis Cai, Hongqiang Harry Liu, Chenren Xu, Ennan Zhai, Yunfei Ma",SIGCOMM,2023,CELLFUSION Vehicle-to-Cloud Streaming Data,ni2023cellfusion
+Converge: QoE-driven Multipath Video Conferencing over WebRTC,"Sandesh Dhawaskar Sathyanarayana, Kyunghan Lee, Dirk Grunwald, Sangtae Ha",SIGCOMM,2023,CONVERGE Multipath Video Conferencing Dataset,dhawaskar2023converge
+Dragonfly: Higher Perceptual Quality For Continuous 360° Video Playback,"Ehab Ghabashneh, Chandan Bothra, Ramesh Govindan, Antonio Ortega, Sanjay Rao",SIGCOMM,2023,"360 Video Dataset, Belgian 4G Mobile Throughput Traces, Irish 5G Mobile Throughput Traces ",ghabashneh2023dragonfly
+Enabling Long-Range Underwater Backscatter via Van Atta Acoustic Networks,"Aline Eid, Jack Rademacher, Waleed Akbar, Purui Wang, Ahmed Allam, and Fadel Adib",SIGCOMM,2023,Van Atta Acoustic Backscatter (VAB) Underwater Dataset ,eid2023enabling
+Enabling Ubiquitous Wi-Fi Sensing with Beamforming Reports,"Chenhao Wu, Xuan Huang, Jun Huang, and Guoliang Xing",SIGCOMM,2023,"City-Scale Wi-Fi Capability Measurement, BeamSense Wi-Fi Sensing Dataset, SignFi Dataset",wu2023enabling
+Resilient Baseband Processing in Virtualized RANs with Slingshot,"Nikita Lazarev, Tao Ji, Anuj Kalia, Daehyeok Kim, Ilias Marinos, Francis Y. Yan, Christina Delimitrou, Zhiru Zhang, Aditya Akella",SIGCOMM,2023,Slingshot 5G vRAN Resilience Dataset,lazarev2023resilient
+Towards Practical and Scalable Molecular Networks,"Jiaming Wang, Sevda Öğüt, Haitham Al Hassanieh, and Bhuvana Krishnaswamy",SIGCOMM,2023,MoMA (Molecular Multiple Access) Dataset,wang2023towards
+Underwater 3D positioning on smart devices,"Tuochao Chen, Justin Chan and Shyamnath Gollakota",SIGCOMM,2023,Underwater Acoustic Positioning Dataset,chen2023underwater
+Mobile Volumetric Video Streaming System through Implicit Neural Representation,"Junhua Liu, Yuanyuan Wang, Yan Wang, Yufeng Wang, Shuguang Cui, Fangxin Wang",SIGCOMM,2023,VOINR Volumetric Video Streaming Performance Dataset,liu2023mobile
+RTCSR: Zero-latency Aware Super-resolution for WebRTC Mobile Video Streaming,"Qian Yu, Qing Li, Rui He, Wanxin Shi, and Yong Jiang",SIGCOMM,2023,"Norway 3G Mobile Network Traces, Belgian 4G Mobile Network Traces",yu2023rtcsr
+The Power of Asynchronous SLAM in Multi-User AR over Cellular Networks: A Measurement Study,"Yuting Guo, Sizhe Wang, Moinak Ghoshal, Y. Charlie Hu, Dimitrios Koutsonikolas",SIGCOMM,2023,Multi-User AR over Cellular Networks Dataset,guo2023power
+Improving BLE Fingerprint Radio Maps: A Method based on Fuzzy Clustering and Weighted Interpolation,"Hannaneh B. Pasandi, David Verde, Azin Moradbeikie, Sara Paiva, Daniel Barros, Sérgio Ivan Lopes",SIGCOMM,2023,BLE Fingerprint Radio Map for Indoor Localization,pasandi2023improving
+DeTagTive: Linking MACs to Protect Against Malicious BLE Trackers,"Tess Despres, Noelle Davis, Prabal Dutta, and David Wagner",SIGCOMM,2023,DeTagTive Malicious BLE Tracker Dataset,despres2023detagtive
+KNEW: Key Generation using NEural Networks from Wireless Channels,Xue Wei and Dola Saha,SIGCOMM,2023,"KNEW Simulated Rayleigh Channel Dataset, KNEW Over-the-Air USRP Wi-Fi Dataset
+",wei2022knew
+Underwater Messaging Using Mobile Devices,"Tuochao Chen, Justin Chan and Shyamnath Gollakota",SIGCOMM,2022,AquaApp Underwater Acoustic Messaging Dataset,chen2022underwater
+Vivisecting mobility management in 5G cellular networks,"Ahmad Hassan, Arvind Narayanan, Anlan Zhang, Wei Ye, Ruiyang Zhu, Shuowei Jin, Jason Carpenter, Z. Morley Mao, Feng Qian, Zhi-Li Zhang ",SIGCOMM,2022,5G Mobility and Performance Drive-Test Dataset ,hassan2022vivisecting
+Understanding 5G performance for real-world services: a content provider's perspective,"Xinjie Yuan, Mingzhou Wu, Zhi Wang, Yifei Zhu, Ming Ma, Junjian Guo, Zhi-Li Zhang, Wenwu Zhu",SIGCOMM,2022,"Kuaishou 5G/4G Passive Performance Dataset,  Kuaishou 5G/4G Active Traceroute Dataset",yuan2022understanding
+"Mobile access bandwidth in practice: measurement, analysis, and implications","Xinlei Yang, Hao Lin, Zhenhua Li, Feng Qian, Xingyao Li, Zhiming He, Xudong Wu, Xianlong Wang, Yunhao Liu, Zhi Liao, Daqiang Hu, Tianyin Xu",SIGCOMM,2022,UUSpeedTest Mobile Bandwidth & Diagnostics Dataset,yang2022mobile
+SEED: a SIM-based solution to 5G failures,"Jinghao Zhao, Zhaowei Tan, Yifei Xu, Zhehui Zhang, Songwu Lu",SIGCOMM,2022,"MobileInsight/MI-LAB Public 5G/4G Signaling Traces, SEED 5G Failure & Recovery Testbed Dataset",zhao2022seed
+Achieving consistent low latency for wireless real-time communications with the shortest control loop,"Zili Meng, Yaning Guo, Chen Sun, Bo Wang, Justine Sherry, Hongqiang Harry Liu, Mingwei Xu",SIGCOMM,2022,"Large-Scale Online RTC Platform Performance Dataset, Real-World WiFi and Cellular Performance Traces,  Zhuge WiFi AP Performance Testbed Dataset",meng2022achieving
+SDN in the Stratosphere: Loon's Aerospace Mesh Network,"Frank Uyeda, Marc Alvidrez, Erik Kline, Bryce Petrini, Brian Barritt, David Mandle, Aswin Chandy Alexander",SIGCOMM,2022,Loon Network Operational & Telemetry Dataset,uyeda2022sdn
+A Case for Stateless Mobile Core Network Functions in Space,"Yuanjie Li, Hewu Li, Wei Liu, Lixin Liu, Yimei Chen, Jianping Wu, Qian Wu, Jun Liu, Zeqi Lai",SIGCOMM,2022,"Geostationary Mobile Satellite Signaling Traces (Inmarsat/Tiantong), Terrestrial 5G Signaling Traces (China Unicom/Mobile/Telecom)",li2022case
+Empowering smart buildings with self-sensing concrete for structural health monitoring,"Zheng Gong, Lubing Han, Zhenlin An, Lei Yang, Siqi Ding, Yu Xiang",SIGCOMM,2022,"EcoCapsule Acoustic Backscatter Performance Dataset, Footbridge Structural Health Monitoring Pilot Study Dataset",gong2022empowering
+Higher-order modulation for acoustic backscatter communication in metals,"Peter Oppermann, Christian Renner",SIGCOMM,2022,Acoustic Backscatter in Metals Performance Dataset,oppermann2022higher
+RF-protect: privacy against device-free human tracking,"Jayanth Shenoy, Zikun Liu, Bill Tao, Zachary Kabelac, Deepak Vasisht",SIGCOMM,2022,RF-Protect Spoofing Performance Dataset,shenoy2022rf
+Cyclops: an FSO-based wireless link for VR headsets,"Himanshu Gupta, Max Curran, Jon Longtin, Torin Rockwell, Kai Zheng, Mallesham Dasari",SIGCOMM,2022,Cyclops FSO Link Performance Dataset,gupta2022cyclops
+An in-depth study of uplink performance of 5G mmWave networks,"Moinak Ghoshal, Z. Jonny Kong, Qiang Xu, Shivang Aggarwal, Imran Khan, Yuanjie Li, Y. Charlie Hu, Dimitrios Koutsonikolas",SIGCOMM,2022,5G mmWave Uplink Performance Dataset ,ghoshal2022depth
+Implications of handover events in commercial 5G non-standalone deployments in Rome,"Konstantinos Kousias, Mohammad Rajiullah, Giuseppe Caso, Ozgu Alay, Anna Brunstrom, Luca De Nardis, Marco Neri, Usman Ali, Maria-Gabriella Di Benedetto",SIGCOMM,2022,5G NSA Handover Performance Dataset,kousias2022implications
+Performance of QUIC congestion control algorithms in 5G networks,"Habtegebreil Haile, Karl-Johan Grinnemo, Simone Ferlin, Per Hurtig, Anna Brunstrom",SIGCOMM,2022,"Karlstad University 5G Throughput Traces, Live 5G QUIC Congestion Control Performance Dataset",haile2022performance
+Prediction and exposure of delays from a base station perspective in 5G and beyond networks,"Akhila Rao, William Tärneberg, Emma Fitzgerald, Lorenzo Corneo, Aleksandr Zavodovski, Omkar Rai, Sixten Johansson, Viktor Berggren, Hassam Riaz, Caner Kilinc, Andreas Johnsson",SIGCOMM,2022,5G mmWave Base Station Metrics and RTT Dataset,rao2022prediction
+5GPerf: profiling open source 5G RAN components under different architectural s,"Cuidi Wei, Ahan Kak, Nakjung Choi, Timothy Wood",SIGCOMM,2022,OAI 5G RAN Performance & Profiling Dataset ,wei20225gperf
+"Application-specific, dynamic reservation of 5G compute and network resources by using reinforcement learning","Anousheh Gholami, Kunal Rao, Wang-Pin Hsiung, Oliver Po, Murugan Sankaradas, John S. Baras, Srimat Chakradhar",SIGCOMM,2022,RL-based 5G Resource Reservation Performance Dataset,gholami2022application
+A preliminary analysis of data collection and retrieval scheme for green information-centric wireless sensor networks,Shintaro Mori,SIGCOMM,2022,Green ICWSN Performance Dataset,mori2022preliminary
+Tooth: Toward Optimal Balance of Video QoE and Redundancy Cost by Fine-Grained FEC in Cloud Gaming Streaming,"Congkai An, Huanhuan Zhang, Shibo Wang, Jingyang Kang, Anfu Zhou, Liang Liu, Huadong Ma, Zili Meng, Delei Ma, Yusheng Dong, Xiaogang Lei",NSDI,2025,Well-Link Cloud Gaming QoE and FEC Performance Dataset,an2025tooth
+CellReplay: Towards accurate record-and-replay for cellular networks,"William Sentosa, Balakrishnan Chandrasekaran, P. Brighten Godfrey, Haitham Hassanieh",NSDI,2025,CellReplay Light and Heavy Workload Traces,sentosa2025cellreplay
+Large Network UWB Localization: Algorithms and Implementation,"Nakul Garg, Irtaza Shahid, Ramanujan K Sheshadri, Karthikeyan Sundaresan, Nirupam Roy",NSDI,2025,"Self-generated UWB localization dataset, Self-generated UWB peer-to-peer measurement traces for simulation",garg2025large
+Towards Energy Efficient 5G vRAN Servers,"Anuj Kalia, Nikita Lazarev, Leyang Xue, Xenofon Foukas, Bozidar Radunovic, Francis Y. Yan",NSDI,2025,"Self-generated LTE TTI-level traffic traces, Self-generated 5G vRAN performance data",kalia2025towards
+Building Massive MIMO Baseband Processing on a Single-Node Supercomputer,"Xincheng Xie, Wentao Hou, Zerui Guo, and Ming Liu ",NSDI,2025,Self-generated massive MIMO IQ samples,xie2025building
+TECC: Towards Efficient QUIC Tunneling via Collaborative Transmission Control,"Jiaxing Zhang, Furong Yang, Ting Liu, Qinghua Wu, Wu Zhao, Yuanbo Zhang, Wentao Chen, Yanmei Liu, Hongyu Guo, Yunfei Ma, Zhenyu Li ",NSDI,2024,Self-generated real-world mobile network traces (Cellular and WiF,zhang2024tecc
+NN-Defined Modulator: Reconfigurable and Portable Software Modulator on IoT Gateways,"Jiazhao Wang, Wenchao Jiang, Ruofeng Liu, Bin Hu, Demin Gao, Shuai Wang",NSDI,2024,"Self-generated over-the-air ZigBee performance data, Self-generated over-the-air WiFi performance data",wang2024nn
+Democratizing Direct-to-Cell Low Earth Orbit Satellite Networks,"Lixin Liu, Yuanjie Li, Hewu Li, Jiabo Yang, Wei Liu, Jingyi Lan, Yufeng Wang, Jiarui Li, Jianping Wu, Qian Wu, Jun Liu, and Zeqi Lai",NSDI,2024,"Self-generated Iridium RF link measurements, Self-generated 3GPP NTN performance data",liu2024democratizing
+Spectrumize: Spectrum-efficient Satellite Networks for the Internet of Things,"Vaibhav Singh, Tusher Chakraborty, Suraj Jog, Om Chabra, Deepak Vasisht, Ranveer Chandra ",NSDI,2024,"Self-generated raw SDR recordings, Self-generated IoT satellite signals",singh2024spectrumize
+Application-Level Service Assurance with 5G RAN Slicing,"Arjun Balasingam, Manikanta Kotaru, Paramvir Bahl ",NSDI,2024,"5G vRAN performance data, Beyond Throughput: The Next Generation: A 5G Dataset with Channel and Context Metrics ",balasingam2024application
+Orthcatter: High-throughput In-band OFDM Backscatter with Over-the-Air Code Division,"Caihui Du, Jihong Yu, Rongrong Zhang, Ju Ren, Jianping An",NSDI,2024,Self-generated OFDM backscatter performance data,du2024orthcatter
+EdgeRIC: Empowering Real-time Intelligent Optimization and Control in NextG Cellular Networks,"Woo-Hyun Ko, Ushasi Ghosh, Ujwal Dinesha, Raini Wu, Srinivas Shakkottai, Dinesh Bharadia",NSDI,2024,Self-generated 5G Channel Quality Indicator traces ,ko2024edgeric
+ADR-X: ANN-Assisted Wireless Link Rate Adaptation for Compute-Constrained Embedded Gaming Devices,"Hao Yin, Murali Ramanujam, Joe Schaefer, Stan Adermann, Srihari Narlanka, Perry Lea, Ravi Netravali, Krishna Chintalapudi ",NSDI,2024,Self-generated WiFi packet traces and channel measurements from emulated gaming sessions,yin2024adr
+RFID+: Spatially Controllable Identification of UHF RFIDs via Controlled Magnetic Fields,"Donghui Dai, Zhenlin An, Zheng Gong, Qingrui Pan, Lei Yang",NSDI,2024,Self-generated UHF RFID signals,dai2024rfid+
+SMUFF: Towards Line Rate Wi-Fi Direct Transport with Orchestrated On-device Buffer Management,"Chengke Wang, Hao Wang, Yuhan Zhou, Yunzhe Ni, Feng Qian, Chenren Xu",NSDI,2024,Wi-Fi Direct performance data from COTS smartphones,wang2024smuff
+NR-Surface: NextG-ready µW-reconfigurable mmWave Metasurface,"Minseok Kim, Namjo Ahn, Song Min Kim",NSDI,2024,Self-generated 24GHz mmWave performance data,kim2024nr
+"Cyclops: A Nanomaterial-based, Battery-Free Intraocular Pressure (IOP) Monitoring System inside Contact Lens","Liyao Li, Bozhao Shang, Yun Wu, Jie Xiong, Xiaojiang Chen, Yaxiong Xie",NSDI,2024,UHF RFID performance and sensing data,li2024cyclops
+Habitus: Boosting Mobile Immersive Content Delivery through Full-body Pose Tracking and Multipath Networking,"Anlan Zhang, Chendong Wang, Yuming Hu, Ahmad Hassan, Zejun Zhang, Bo Han, Feng Qian, Shichang Xu",NSDI,2024,Habitus Motion and Wireless Performance Dataset,zhang2024habitus
+BFMSense: WiFi Sensing Using Beamforming Feedback Matrix,"Enze Yi, Dan Wu, Jie Xiong, Fusang Zhang, Kai Niu, Wenwei Li, Daqing Zhang ",NSDI,2024,"BFMSense Experimental BFM Dataset, BFMSense WARP V3 CSI Comparison Dataset",yi2024bfmsense
+mmComb: High-speed mmWave Commodity WiFi Backscatter,"Yoon Chae, Zhenzhe Lin, Kang Min Bae, Song Min Kim, Parth Pathak",NSDI,2024,mmComb Backscatter-Modulated 60 GHz Waveform DatasetBibTeX Citation Key,chae2024mmcomb
+Sidekick: In-Network Assistance for Secure End-to-End Transport Protocols,"Gina Yuan, Matthew Sotoudeh, David K. Zhang, Michael Welzl, David Mazières, Keith Winstein ",NSDI,2024,Sidekick Real-World Wi-Fi/Cellular Performance Data,yuan2024sidekick
+Catch Me If You Can: Laser Tethering with Highly Mobile Targets,"Charles J. Carver, Hadleigh Schwartz, Qijia Shao, Nicholas Shade, Joseph P. Lazzaro, Xiaoxin Wang, Jifeng Liu, Eric R. Fossum, Xia Zhou ",NSDI,2024,Lasertag Optical Tracking and Predictive Steering Dataset,carver2023catch
+Passengers’ Safety Matters: Experiences of Deploying a Large-Scale Indoor Delivery Monitoring System,"Xiubin Fan, Zhongming Lin, Yuming Hu, Tianrui Jiang, Feng Qian, Zhimeng Yin, S.-H. Gary Chan, Dapeng Wu",NSDI,2024,DeMo Indoor Delivery Monitoring Dataset,fan2024passengers
+AUGUR: Practical Mobile Multipath Transport Service for Low Tail Latency in Real-Time Streaming,"Yuhan Zhou, Tingfeng Wang, Liying Wang, Nian Wen, Rui Han, Jing Wang, Chenglei Wu, Jiafeng Chen, Longwei Jiang, Shibo Wang, Honghao Liu, Chenren Xu",NSDI,2024,Tencent Start Cloud Gaming User Session Traces,zhou2024augur
+Cloud-LoRa: Enabling Cloud Radio Access LoRa Networks Using Reinforcement Learning Based Bandwidth-Adaptive Compression,"Muhammad Osama Shahid, Daniel Koch, Jayaram Raghuram, Bhuvana Krishnaswamy, Krishna Chintalapudi, Suman Banerjee",NSDI,2024,Cloud-LoRa Outdoor Deployment I/Q Dataset,shahid2024cloud
+LeakyScatter: A Frequency-Agile Directional Backscatter Network Above 100 GHz,Atsutse Kludze and Yasaman Ghasempour,NSDI,2023,LeakyScatter Sub-THz Backscatter Link Characterization Dataset,kludze2023leakyscatter
+RF-Bouncer: A Programmable Dual-band Metasurface for Sub-6 Wireless Networks,"Xinyi Li, Chao Feng, Xiaojing Wang, Yangfan Zhang, Yaxiong Xie, Xiaojiang Chen",NSDI,2023,RF-Bouncer Dual-Band Metasurface Performance Dataset,li2023rf
+DChannel: Accelerating Mobile Applications With Parallel High-bandwidth and Low-latency Channels,"William Sentosa, Balakrishnan Chandrasekaran, P. Brighten Godfrey, Haitham Hassanieh, Bruce Maggs",NSDI,2023,DChannel 5G eMBB Performance Traces,sentosa2023dchannel
+OpenLoRa: Validating LoRa Implementations through an Extensible and Open-sourced Framework,"Manan Mishra, Daniel Koch, Muhammad Osama Shahid, Bhuvana Krishnaswamy, Krishna Chintalapudi, Suman Banerjee",NSDI,2023,OpenLoRa Benchmark Datasets,mishra2023openlora
+VECARE: Statistical Acoustic Sensing for Automotive In-Cabin Monitoring,"Yi Zhang, Weiying Hou, Zheng Yang, Chenshu Wu",NSDI,2023,VECARE Statistical Acoustic Sensing Dataset,zhang2023vecare
+SlimWiFi: Ultra-Low-Power IoT Radio Architecture Enabled by Asymmetric Communication,"Renjie Zhao, Kejia Wang, Kai Zheng, Xinyu Zhang, Vincent Leung",NSDI,2023,SlimWiFi Asymmetric Communication Performance Dataset,zhao2023slimwiif
+SLNet: A Spectrogram Learning Neural Network for Deep Wireless Sensing,"Zheng Yang, Yi Zhang, Kun Qian, Chenshu Wu",NSDI,2023,Widar 3.0: WiFi-based Activity Recognition Dataset,yang2023slnet
+POLYCORN: Data-driven Cross-layer Multipath Networking for High-speed Railway through Composable Schedulerlets,"Yunzhe Ni, Feng Qian, Taide Liu, Yihua Cheng, Zhiyao Ma, Jing Wang, Zhongfeng Wang, Gang Huang, Xuanzhe Liu, Chenren Xu",NSDI,2023,HSRNet Dataset,ni2023polycorn
+Augmenting Augmented Reality with Non-Line-of-Sight Perception,"Tara Boroushaki, Maisy Lam, Laura Dodds, Aline Eid, Fadel Adib",NSDI,2023,X-AR RF-Visual Localization Dataset,boroushaki2023augmenting
+Acoustic Sensing and Communication Using Metasurface,"Yongzhao Zhang, Yezhou Wang, Lanqing Yang, Mei Wang, Yi-Chao Chen, Lili Qiu, Yihong Liu, Guangtao Xue, Jiadi Yu",NSDI,2023,Acoustic Metasurface Beamforming Performance Dataset,zhang2023acoustic
+Enabling High Quality Real-Time Communications with Adaptive Frame-Rate,"Zili Meng, Tingfeng Wang, Yixin Shen, Bo Wang, Mingwei Xu, Rui Han, Honghao Liu, Venkat Arun, Hongxin Hu, Xue Wei",NSDI,2023,Tencent START Cloud Gaming Network Traces,meng2023enabling
+"CellDAM: User-Space, Rootless Detection and Mitigation for 5G Data Plane","Yifan Chen, Zirui Chen, Zhaowei Tan, R. K. Guduru, Feng Qian, Z. Morley Mao",NSDI,2023,"5G Mobile Carrier Performance Dataset, Private 5G Testbed Performance Data",tan2023celldam
+"mmWall: A Steerable, Transflective Metamaterial Surface for NextG mmWave Networks","Kun Woo Cho, Mohammad H. Mazaheri, Jeremy Gummeson, Omid Abari, and Kyle Jamieson",NSDI,2023,mmWall Metamaterial Beam-Steering Performance Dataset,cho2023mmwall
+Channel-Aware 5G RAN Slicing with Customizable Schedulers,"Yongzhou Chen, Ruihao Yao, Haitham Hassanieh, Radhika Mittal",NSDI,2023,"Modified 5G Cellular Traces from LTScope, Self-generated Synthetic 5G CQI Traces",chen2023channel
+Exploring Practical Vulnerabilities of Machine Learning-based Wireless Systems,"Zikun Liu, Changming Xu, Emerson Sie, Gagandeep Singh, Deepak Vasisht",NSDI,2023,"Self-generated Wi-Fi CSI Data for FIRE System Attack, DLoc Indoor Localization Dataset ",liu2023exploring
+RF-CHORD: Towards Deployable RFID Localization System for Logistics Network,"Bo Liang, Purui Wang, Renjie Zhao, Heyu Guo, Pengyu Zhang, Junchen Guo, Shunmin Zhu, Hongqiang Harry Liu, Xinyu Zhang, Chenren Xu",NSDI,2023,"RF-CHORD Wideband RFID Channel Information Dataset, RF-CHORD Warehouse Operational Dataset, RF-CHORD Fresh Food Delivery Store Operational Dataset",liang2023rf
+5G Metamorphosis: A Longitudinal Study of 5G Performance from the Beginning,"Omar Basit, Imran Khan, Moinak Ghoshal, Y. Charlie Hu, and Dimitrios Koutsonikolas ",IMC,2025,Ookla Speedtest Intelligence 5G/LTE Performance Dataset (2020-2023),basit2025metamorphosis
+Measuring Network Latency from a Wireless ISP: Variations Within and Across Subnets,"Simon Sundberg, Anna Brunstrom, Simone Ferlin-Reiter, Toke Høiland-Jørgensen, and Robert Chacón",IMC,2024,JackRabbit Wireless ISP Latency and Traffic Dataset,sundberg2024measuring
+A Longitudinal Study of the Prevalence of WiFi Bottlenecks in Home Access Networks,"Ranya Sharma, Nick Feamster, and Marc Richardson",IMC,2024,Netrics Home WiFi vs. ISP Throughput Dataset,sharma2024longitudinal
+Characterizing the Security Facets of IoT Device Setup,"Han Yang, Carson Kuzniar, Chengyan Jiang, Ioanis Nikolaidis, and Israat Haque",IMC,2024,IoT Device Setup Wireless Traffic Traces,yang2024characterizing
+Through the Telco Lens: A Countrywide Empirical Study of Cellular Handovers,"Michail Kalntis, José Suárez-Varela, Jesús Omaña Iglesias, Anup Kiran Bhattacharjee, George Iosifidis, Fernando A. Kuipers, and Andra Lutu",IMC,2024,MNO Countrywide Cellular Handover Dataset,kalntis2024through
+"I Tag, You Tag, Everybody Tags!","Hazem Ibrahim, Rohail Asim, Matteo Varvello, and Yasir Zaki",IMC,2023,Apple AirTag and Samsung SmartTag Location Tracking Dataset,ibrahim2023tag
+Characterizing Mobile Service Demands at Indoor Cellular Networks,"Stefanos Bakirtzis, André Felipe Zanella, Stefania Rubrichi, Cezary Ziemlicki, Zbigniew Smoreda, Ian Wassell, Jie Zhang, and Marco Fiore",IMC,2023,MNO Countrywide Indoor Cellular Network Traffic Dataset,bakirtzis2023characterizing
+Modeling and Generating Control-Plane Traffic for Cellular Networks,"Jiayi Meng, Jingqi Huang, Y. Charlie Hu, Yaron Koral, Xiaojun Lin, Muhammad Shahbaz, and Abhigyan Sharma",IMC,2023,US Carrier LTE Control-Plane Traffic Trace,meng2023modeling
+Performance of Cellular Networks on the Wheels,"Moinak Ghoshal, Imran Khan, Z. Jonny Kong, Phuc Dinh, Jiayi Meng, Y. Charlie Hu, and Dimitrios Koutsonikolas",IMC,2023,Cellular Networks on the Wheels Driving Dataset,ghoshal2023performance
+Characterizing and Modeling Session-Level Mobile Traffic Demands from Large-Scale Measurements,"André Felipe Zanella, Antonio Bazco-Nogueras, Cezary Ziemlicki, and Marco Fiore",IMC,2023,MNO Nationwide Session-Level Traffic Dataset,zanella2023characterizing
+A First Look at Starlink Performance,"François Michel, Martino Trevisan, Danilo Giordano, Olivier Bonaventure",IMC,2022,"Self-generated Starlink LEO Satellite Performance Dataset, Self-generated Geostationary SatCom Performance Dataset",michel2022first
+Deep Dive into the IoT Backend Ecosystem,"Said Jawad Saidi, Srdjan Matic, Oliver Gasser, Georgios Smaragdakis, Anja Feldmann",IMC,2022,Passive IoT Traffic Flows from a Major European ISP,saidi2022deep
+When Satellite is All You Have: Watching the Internet from 550 ms,"Daniel Perdices, Gianluca Perna, Martino Trevisan, Danilo Giordano, Marco Mellia",IMC,2022,Passive Geostationary SatCom Traffic Dataset (Europe/Africa),perdices2022satellite
+The Importance of Contextualization of Crowdsourced Active Speed Test Measurements,"Udit Paul, Jiamo Liu, Mengyang Gu, Arpit Gupta, Elizabeth Belding",IMC,2022,"Ookla Speedtest Intelligence Dataset, M-Lab NDT Speed Test Dataset",paul2022importance
+Aurora: Conformity-based Configuration Recommendation to Improve LTE/5G Service,"Ajay Mahimkar, Zihui Ge, Xuan Liu, Yusef Shaqalle, Yu Xiang, Jennifer Yates, Shomik Pathak, Rick Reichel",IMC,2022,Operational LTE/5G Cellular Network Dataset,mahimkar2022aurora
+A Browser-side View of Starlink Connectivity,"Mohamed M. Kassem, Aravindh Raman, Diego Perino, Nishanth Sastry",IMC,2022,"Crowdsourced Starlink Web Performance Dataset, Starlink Active Measurement Dataset (Volunteer Nodes)",kassem2022browser
+Analyzing Real-time Video Delivery over Cellular Networks for Remote Piloting Aerial Vehicles,"Aygün Baltaci, Hendrik Cech, Nitinder Mohan, Fabien Geyer, Vaibhav Bajpai, Jörg Ott, Dominic Schupke",IMC,2022,UAV Real-time Video over LTE Performance Dataset ,baltaci2022analyzing