Reliable 60 GHz WLANs through Coordination: Measurement, Modeling and Optimization

Synopsis

60 GHz millimeter-wave (mmWave) wireless networks have the potential to provide always-on high data rates to support emerging applications such as augmented/virtual reality and high-definition video streaming. However, developing a practical solution to achieve this goal is challenging due to directionality and severe performance degradations introduced by interference, blockages, and mobility. A promising solution is utilizing a dense deployment where access points (APs) can serve mobile users and combat interference and blockages in a coordinated way to achieve high capacity and reliability. This project used measurements, modeling, and optimization to design, analyze and evaluate novel cooperative beamforming and link scheduling techniques to enable dense mmWave networks. In particular, the project developed a novel online learning framework for joint beamforming and scheduling for throughput optimization in mmWave WLANs, which was validated using data collected from real-world mmWave deployments.

Personnel

• Principal Investigators: Zizhan Zheng (Tulane), Parth Pathak (George Mason)
• Graduate Research Associates: Tianyi Xu (Tulane), Henger Li (Tulane), Ding Zhang (George Mason)

Broader Impacts

The project has provided unique training experiences to three graduate students. They were exposed to diverse topics on wireless networking, online learning, reinforcement learning, and optimization and obtained much-needed analytical and empirical skills. The project findings were incorporated into the newly developed reinforcement learning course at Tulane University and provided topics for student presentations and term projects. The project outcomes were disseminated via talks and posters at workshops and conferences.

Publications

• CoBF: Coordinated Beamforming in Dense mmWave Networks
  Ding Zhang, Panneer Selvam Santhalingam, Parth Pathak, Zizhan Zheng
  ACM International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS), June 2023.
• Online Learning for Adaptive Probing and Scheduling in Dense WLANs
  Tianyi Xu, Ding Zhang, and Zizhan Zheng
  IEEE International Conference on Computer Communications (INFOCOM), June 2023.
• Towards Optimal Tradeoff Between Data Freshness and Update Cost in Information-update Systems
  Zhongdong Liu, Bin Li, Zizhan Zheng, Y. Thomas Hou, and Bo Ji
  IEEE Internet of Things Journal (IoTJ), 2023.
• Networked Beamforming in Dense mmWave WLANs
  Ding Zhang, Panneer Selvam Santhalingam, Parth Pathak, and Zizhan Zheng
  International Workshop on Mobile Computing Systems and Applications (ACM HotMobile), Mar. 2022.
• Joint AP Probing and Scheduling: A Contextual Bandit Approach.
  Tianyi Xu, Ding Zhang, Parth Pathak, and Zizhan Zheng
  IEEE Military Communications Conference (MILCOM), Nov. 2021.
• Characterizing Interference Mitigation Techniques in Dense 60 GHz mmWave WLANs.
  Ding Zhang, Panneer Selvam Santhalingam, Parth Pathak, and Zizhan Zheng
  International Conference on Computer Communications and Networks (ICCCN), July 2019.

Support

The project is funded by National Science Foundation (NSF) grant award CNS-1816943.

Disclaimer: Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.