Ph.D. Dissertation Defense - Yuchen Liu

Title:  Location, Location, Location: Maximizing mmWave LAN Performance through Intelligent Wireless Networking Strategies
Dr. Douglas Blough, ECE, Chair, Advisor
Dr. Karthik Sundaresan, ECE
Dr. Ragupathy Sivakumar, ECE
Dr. Henry Owen, ECE
Dr. Shiwen Mao, Auburn Univ.
Abstract: The main objective of this dissertation is to design and evaluate intelligent techniques to maximize mmWave wireless local-area network (WLAN) performance. To meet the ever-increasing data demand of various bandwidth-hungry applications, we propose techniques to enable consistently ultra-high-rate mmWave communication in the wireless environment. However, the weak diffraction of mmWave signals makes them extremely sensitive to blockage effects caused by real-world obstacles, and this is a primary challenge to overcome for the feasibility of mmWave communications. To this end, we exploit location sensitivity to explore robust mmWave WLAN designs that expedite the full realization of ubiquitous mmWave wireless connectivity. The techniques investigated to exploit location sensitivity are the use of multiple access points (APs), controlled mobility, AP-user association mechanisms, and environment-aware prediction We first develop optimal multi-AP planning approaches to maximize line-of-sight connectivity and aggregate throughput in mmWave WLANs, and then study multi-AP association mechanisms to achieve low-overhead and blockage-robust mmWave wireless communications among multiple users and multiple APs. Furthermore, we explore the potential benefits achievable from AP mobility technology, which yields insights on the best configurations of mobile APs. We also develop an environment-aware link-quality predictor to accurately derive dynamic mmWave link quality due to static blockages and small changes in device locations, which provides a basis for the development of anticipatory networking with proactive resource-allocation schemes. In a complementary direction for evaluating the performance of mmWave networks, we develop and implement advanced features for dense wireless networks that increasingly characterize many mmWave scenarios of interest in the widely-used network simulator ns-3, including a sparse cluster-based wireless channel model that statistically models multi-path components in mmWave WLANs.

Event Details


  • Thursday, June 2, 2022
    10:00 am - 12:00 pm

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