List of Personnel

Principal Investigator: Jia (Kevin) Liu

Intellectual Merits

The potential of Massive MIMO technologies to make a significant impact on the next generation multi-Gigabit wireless communication networks is immense. The proposed research will not only advance the knowledge in the design of Massive MIMO wireless networks but will also serve a critical need in the general networking research community by exploring a network-level understanding of Massive MIMO networks through a unified research program that consists of the development of tractable cross-layer theoretical models, exploration of theoretical performance bounds and capacity limits, and the development of distributed algorithms. The proposed research aims to close the gap between advances in physical layer Massive MIMO communications technologies and wireless networking research. The proposed research will support the networking community and the general public by facilitating the development of Massive MIMO networks with substantially increased network performance.

Major Activities

1. Efficient Scheduling Design for Massive MIMO Cellular Networks

   In this thrust, we focus on multi-user scheduling problems at the link layer in cellular networks, where the base stations are equipped with Massive MIMO systems. In Massive MIMO cellular networks, the main challenge of the multi-user scheduling problem stems from the imperfect or incomplete channel state information (CSI), which is critical for all opportunistic scheduling designs. In this project, our goal is to develop efficient scheduling policies that can adapt to the CSI availability and offer: (i) provable throughput-optimality, (ii) asymptotic rate-function delay optimality, and (iii) low complexity.

2. Optimal Routing and Congestion Control for Massive MIMO Multi-Hop Networks

   In this research thrust, we will concentrate on the joint multi-hop routing and congestion control optimization problems in Massive MIMO multi-hop networks. This research is particularly relevant for wireless back-haul networks, where each link employs Massive MIMO. Here, our goal will be to ensure that not only are the end-to-end session rates utility-optimal under our proposed joint multi-hop routing and congestion control algorithm with imperfect and/or incomplete CSI, but also that all routing and congestion decision variables converge to the optimal solution with a fast speed.

3. Energy Analytics for Massive MIMO Wireless Networks

   In the aforementioned thrusts, we have focused on the throughput and delay performances of our proposed control and optimization algorithms. In this thrust, we are concerned with the energy expenditure of Massive MIMO wireless networks. The energy expenditure performance is important because the energy consumption of cellular base stations has become a growing concern in recent years and there is a compelling need for wireless networks to go green. Unlike conventional MIMO power minimization problems, in this project, we are interested in Massive MIMO base stations equipped with emerging green technologies (e.g., renewable energy sources and storage). The time-varying nature of energy costs, traffic-load, and renewable energy-supply would significantly complicate the power management of Massive MIMO networks.

Products

1. J. Liu, A. Eryilmaz, N. B. Shroff, and E. S. Bentley, "Heavy-Ball: A New Approach for Taming Delay and Convergence in Wireless Network Optimization," in Proc. IEEE INFOCOM, San Francisco, CA, Apr. 2016 (Best Paper Award, acceptance rate: 17%).


2. J. Liu, A. Eryilmaz, N. B. Shroff, and E. S. Bentley, "Understanding the Impact of Limited Channel State Information on Massive MIMO Network Performances," in Proc. ACM MobiHoc, Paderborn, Germany, July 2016 (acceptance rate: 17%).


3. J. Liu, "Achieving Low-Delay and Fast-Convergence in Stochastic Network Optimization: A Nesterovian Approach," in Proc. ACM Sigmetrics, Antibes Juan-les-Pins, Jun. 2016 (acceptance rate: 13%).


4. J. Liu, N. B. Shroff, C. H. Xia, H. D. Sherali, "Joint Congestion Control and Routing Optimization: An Efficient Second-Order Distributed Approach,'' IEEE/ACM Transactions on Networking, vol. 24, no. 3, pp. 1404-1420, Jun. 2016.