Low-Complexity and Robust Quantized Hybrid Beamforming and Channel Estimation

Hybrid beamforming with phase shifters and switches has been identified as a low-cost and energy-efficient approach to harness the benefits of massive multiple-input multiple-output (MIMO) systems. In this paper, three subconnected hybrid beamforming structures with different combinations of phase shifters and switches will be considered. Firstly we assume that perfect channel state information (CSI) is available and the wireless channel follows uncorrelated Rayleigh fading model. Then, we derive the closed-form expressions of the low-complexity beamformers and their asymptotic achievable sum-rates. Based on the proposed beamformers, we develop quantized hybrid beamforming and channel estimation techniques for correlated Rayleigh fading channels. These methods rely on designing novel RF codebooks and they can be used in both CSI acquisition and data transmission phases. The proposed methods benefit from low computational complexity, low signaling overhead and robustness to estimation errors. Moreover, they are applicable to both frequency and time division duplex systems

Hybrid Precoding for Multiuser Millimeter Wave Massive MIMO Systems : A Deep Learning Approach

© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.In multi-user millimeter wave (mmWave) multiple-input-multiple-output (MIMO) systems, hybrid precoding is a crucial task to lower the complexity and cost while achieving a sufficient sum-rate. Previous works on hybrid precoding were usually based on optimization or greedy approaches. These methods either provide higher complexity or have sub-optimum performance. Moreover, the performance of these methods mostly relies on the quality of the channel data. In this work, we propose a deep learning (DL) framework to improve the performance and provide less computation time as compared to conventional techniques. In fact, we design a convolutional neural network for MIMO (CNN-MIMO) that accepts as input an imperfect channel matrix and gives the analog precoder and combiners at the output. The procedure includes two main stages. First, we develop an exhaustive search algorithm to select the analog precoder and combiners from a predefined codebook maximizing the achievable sum-rate. Then, the selected precoder and combiners are used as output labels in the training stage of CNN-MIMO where the input-output pairs are obtained. We evaluate the performance of the proposed method through numerous and extensive simulations and show that the proposed DL framework outperforms conventional techniques. Overall, CNN-MIMO provides a robust hybrid precoding scheme in the presence of imperfections regarding the channel matrix. On top of this, the proposed approach exhibits less computation time with comparison to the optimization and codebook based approaches.Peer reviewe