TY - JOUR
T1 - Deep Learning for SVD and Hybrid Beamforming
AU - Peken, Ture
AU - Adiga, Sudarshan
AU - Tandon, Ravi
AU - Bose, Tamal
N1 - Funding Information:
Manuscript received July 22, 2019; revised February 28, 2020; accepted June 13, 2020. Date of publication June 30, 2020; date of current version October 9, 2020. This work was supported in part by the Broadband Wireless Access and Applications Center (BWAC) and in part by NSF Award 1822071. The work of Ravi Tandon was supported in part by NSF under Grant CAREER 1651492 and Grant CNS 1715947 and in part by the 2018 Keysight Early Career Professor Award. The associate editor coordinating the review of this article and approving it for publication was E. De Carvalho. (Corresponding author: Ravi Tandon.) The authors are with the Department of Electrical and Computer Engineering, University of Arizona, Tucson, AZ 85721 USA (e-mail: turepeken@email.arizona.edu; adiga@email.arizona.edu; tandonr@email. arizona.edu; tbose@email.arizona.edu).
PY - 2020/10
Y1 - 2020/10
N2 - Hybrid beamforming (BF), which divides BF operation into radio frequency (RF) and baseband (BB) domains, will play a critical role in MIMO communication at millimeter-wave (mmW) frequencies. In principle, we can obtain unconstrained (optimum) beamformers of a transceiver, which approach the maximum achievable data rates, through its singular value decomposition (SVD). Due to the use of finite-precision phase shifters, combined with power constraints, additional challenges are imposed on the problem of designing hybrid beamformers. Motivated by the recent success of machine learning (ML) techniques, particularly in areas such as computer vision and speech recognition, we explore if ML techniques can be effectively used for SVD and hybrid BF. To this end, we first present a data-driven approach to compute the SVD. We propose three deep neural network (DNN) architectures to approximate the SVD, with varying levels of complexity. The methodology for training these DNN architectures is inspired by the fundamental property of SVD, i.e., it can be used to obtain low-rank approximations. We next explicitly take the constraints of hybrid BF into account (such as quantized phase shifters, power constraints), and propose a novel DNN based approach for the design of hybrid BF systems. To validate the DNN based approach, we present simulation results for both approximating the SVD as well as for hybrid BF. Our results show that DNNs can be an attractive and efficient solution for estimating SVD in a data-driven manner. For the simulations of hybrid BF, we first consider the geometric channel model. We show that the DNN based hybrid BF improves rates by up to 50-70% compared to conventional hybrid BF algorithms and achieves 10-30% gain in rates compared with the state-of-Art ML-Aided hybrid BF algorithms. We also discuss the impact of the choice of hyperparameters, such as the number of hidden layers, mini-batch size, and training iterations on the accuracy of DNNs. Furthermore, we provide time complexity and memory requirement analyses for the proposed approach and state-of-The-Art approaches.
AB - Hybrid beamforming (BF), which divides BF operation into radio frequency (RF) and baseband (BB) domains, will play a critical role in MIMO communication at millimeter-wave (mmW) frequencies. In principle, we can obtain unconstrained (optimum) beamformers of a transceiver, which approach the maximum achievable data rates, through its singular value decomposition (SVD). Due to the use of finite-precision phase shifters, combined with power constraints, additional challenges are imposed on the problem of designing hybrid beamformers. Motivated by the recent success of machine learning (ML) techniques, particularly in areas such as computer vision and speech recognition, we explore if ML techniques can be effectively used for SVD and hybrid BF. To this end, we first present a data-driven approach to compute the SVD. We propose three deep neural network (DNN) architectures to approximate the SVD, with varying levels of complexity. The methodology for training these DNN architectures is inspired by the fundamental property of SVD, i.e., it can be used to obtain low-rank approximations. We next explicitly take the constraints of hybrid BF into account (such as quantized phase shifters, power constraints), and propose a novel DNN based approach for the design of hybrid BF systems. To validate the DNN based approach, we present simulation results for both approximating the SVD as well as for hybrid BF. Our results show that DNNs can be an attractive and efficient solution for estimating SVD in a data-driven manner. For the simulations of hybrid BF, we first consider the geometric channel model. We show that the DNN based hybrid BF improves rates by up to 50-70% compared to conventional hybrid BF algorithms and achieves 10-30% gain in rates compared with the state-of-Art ML-Aided hybrid BF algorithms. We also discuss the impact of the choice of hyperparameters, such as the number of hidden layers, mini-batch size, and training iterations on the accuracy of DNNs. Furthermore, we provide time complexity and memory requirement analyses for the proposed approach and state-of-The-Art approaches.
KW - Hybrid beamforming
KW - machine learning
KW - massive MIMO
KW - millimeter-waves
KW - singular value decomposition
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U2 - 10.1109/TWC.2020.3004386
DO - 10.1109/TWC.2020.3004386
M3 - Article
AN - SCOPUS:85089416976
VL - 19
SP - 6621
EP - 6642
JO - IEEE Transactions on Wireless Communications
JF - IEEE Transactions on Wireless Communications
SN - 1536-1276
IS - 10
M1 - 9130130
ER -