Browsing by Author "Chen, Yongce"

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    Modeling, Analysis, and Real-Time Design of Many-Antenna MIMO Networks
    Chen, Yongce (Virginia Tech, 2021-09-14)
    Among the many advances and innovations in wireless technologies over the past twenty years, MIMO is perhaps among the most successful. MIMO technology has been evolving over the past two decades. Today, the number of antennas equipped at a base station (BS) or an access point (AP) is increasing, which forms what we call ``many-antenna'' MIMO systems. Many-antenna MIMO will have significant impacts on modern wireless communications, as it will allow numerous wireless applications to operate on the vastly underexplored mid-band and high-band spectrum and is able to deliver ultra-high throughput. Although there are considerable efforts on many-antenna MIMO systems, most of them came from physical (PHY) layer information-theoretic exploitation. There is a lack of investigation of many-antenna MIMO from a networking perspective. On the other hand, new knowledge and understanding begin to emerge at the PHY layer, such as the rank-deficient channel phenomenon. This calls for new theories and models for many-antenna MIMO in a networking environment. In addition, the problem space for many-antenna MIMO systems is much broader and more challenging than conventional MIMO. Reusing existing solutions designed for conventional MIMO systems may suffer from inferior performance or require excessive computation time. The goal of this dissertation is to advance many-antenna MIMO techniques for networking research. We focus on the following two critical areas in the context of many-antenna MIMO networks: (i) DoF-based modeling and (ii) real-time optimization. This dissertation consists of two parts that study these two areas. In the first part, we aim to develop new DoF models and theories under general channel rank conditions for many-antenna MIMO networks, and we explored efficient DoF allocation based on our new DoF model. The main contributions of this part are summarized as follows. New DoF models and theories under general channel rank conditions: Existing DoF-based models in networking community assume that the channel matrix is of full rank. However, this assumption no longer holds when the number of antennas becomes many and the propagation environment is not ideal. In this study, we develop a novel DoF model under general channel rank conditions. In particular, we find that for IC, shared DoF consumption at both transmit and receive nodes is most efficient for DoF allocation, which is contrary to existing unilateral IC models based on full-rank channel assumption. Further, we show that existing DoF models under the full-rank assumption are a special case of our generalized DoF model. The findings of this study pave the way for future research of many-antenna networks under general channel rank conditions. Efficient DoF utilization for MIMO networks: We observes that, in addition to the fact that channel is not full-rank, the strength of signals on different directions in the eigenspace is extremely uneven. This offers us new opportunities to efficiently utilize DoFs in a MIMO network. In this study, we introduce a novel concept called ``effective rank threshold''. Based on this threshold, DoFs are consumed only to cancel strong interferences in the eigenspace while weak interferences are treated as noise in throughput calculation. To better understand the benefits of this approach, we study a fundamental trade-off between network throughput and effective rank threshold for an MU-MIMO network. Our simulation results show that network throughput under optimal rank threshold is significantly higher than that under existing DoF IC models. In the second part, we offered real-time designs and implementations to solve many-antenna MIMO problems for 5G cellular systems. In addition to maximizing a specific optimization objective, we aim at offering a solution that can be implemented in sub-ms to meet requirements in 5G standards. The main contributions of this part are summarized as follows. Turbo-HB---A novel design and implementation for ultra-fast hybrid beamforming: We investigate the beamforming problem under hybrid beamforming (HB) architecture. A major practical challenge for HB is to obtain a solution in 500 $mu$s, which is an extremely stringent but necessary time requirement for its deployment in the field. To address this challenge, we present Turbo-HB---a novel beamforming design under the HB architecture that can obtain the beamforming matrices in about 500 $mu$s. The key ideas of Turbo-HB are two-fold. First, we develop low-complexity SVD by exploiting randomized SVD technique and leveraging channel sparsity at mmWave frequencies. Second, we accelerate the overall computation time through large-scale parallel computation on a commercial off-the-shelf (COTS) GPU platform, with special engineering efforts for matrix operations and minimized memory access. Experimental results show that Turbo-HB is able to obtain the beamforming matrices in 500 $mu$s for an MU-MIMO cellular system while achieving similar or better throughput performance by those state-of-the-art algorithms. mCore+---A sub-millisecond scheduler for 5G MU-MIMO systems: We study a scheduling problem in a 5G NR environment. In 5G NR, an MU-MIMO scheduler needs to allocate RBs and assign MCS for each user at each TTI. In particular, multiple users may be co-scheduled on the same RB under MU-MIMO. In addition, the real-time requirement for determining a scheduling solution is at most 1 ms. In this study, we present a novel scheduler mCore+ that can meet the sub-ms real-time requirement. mCore+ is designed through a multi-phase optimization, leveraging large-scale parallelism. In each phase, mCore+ either decomposes the optimization problem into a large number of independent sub-problems, or reduces the search space into a smaller but more promising subspace, or both. We implement mCore+ on a COTS GPU platform. Experimental results show that mCore+ can obtain a scheduling solution in $sim$500 $mu$s. Moreover, mCore+ can achieve better throughput performance than state-of-the-art algorithms. M3---A sub-millisecond scheduler for multi-cell MIMO networks under C-RAN architecture: We investigate a scheduling problem for a multi-cell environment. Under Cloud Radio Access Network (C-RAN) architecture, the signal processing can be performed cooperatively for multiple cells at a centralized baseband unit (BBU) pool. However, a new resource scheduler is needed to jointly determine RB allocation, MCS assignment, and beamforming matrices for all users under multiple cells. In addition, we aim at finding a scheduling solution within each TTI (i.e., at most 1 ms) to conform to the frame structure defined by 5G NR. To do this, we propose M3---a GPU-based real-time scheduler for a multi-cell MIMO system. M3 is developed through a novel multi-pipeline design that exploits large-scale parallelism. Under this design, one pipeline performs a sequence of operations for cell-edge users to explore joint transmission, and in parallel, the other pipeline is for cell-center users to explore MU-MIMO transmission. For validation, we implement M3 on a COTS GPU. We showed that M3 can find a scheduling solution within 1 ms for all tested cases, while it can significantly increase user throughput by leveraging joint transmission among neighboring cells.