Hybrid Architecture and Beamforming Optimization for Millimeter Wave Systems

doi:10.12142/ZTECOM.202303013

ZTE Communications ›› 2023, Vol. 21 ›› Issue (3): 93-104.DOI: 10.12142/ZTECOM.202303013

收稿日期:2023-02-21 出版日期:2023-09-21 发布日期:2023-09-21

Hybrid Architecture and Beamforming Optimization for Millimeter Wave Systems

TANG Yuanqi¹, ZHANG Huimin¹, ZHENG Zheng², LI Ping², ZHU Yu¹()

^1.Department of Communication Science and Engineering, Fudan University, Shanghai 200433, China
^2.ZTE Corporation, Shenzhen 518057, China

Received:2023-02-21 Online:2023-09-21 Published:2023-09-21
About author:TANG Yuanqi received her BS degree in communication science and engineering from Fudan University, China in 2022, where she is currently pursuing her MS degree. Her research interests include hybrid beamforming for massive MIMO systems, millimeter wave signal processing and reconfigurable intelligent surface.|ZHANG Huimin received her BS degree in communication science and engineering from Fudan University, China in 2021, where she is currently pursuing her MS degree. Her current research interests include hybrid beamforming for massive MIMO systems and energy efficiency in intelligent reflecting surface-aided systems.|ZHENG Zheng received his BS and PhD degrees in information science and electronic engineering from Zhejiang University, China in 2013 and 2019, respectively. He is currently a senior algorithm engineer working on physical layer algorithms in ZTE Corporation. His research interests include wireless communications, array signal processing and artificial intelligence algorithms.|LI Ping received her MS degree in communication and information engineering from Xi’an Jiaotong University, China in 2004. She is currently a senior algorithm system engineer at ZTE Corporation, responsible for national key projects. Her research interests include digital signal processing, multiple antenna, system performance optimization, reconfigurable intelligent surface, networking technology, network planning, integrated sensing and communications (ISAC), and key technologies in 5G-A. She has applied for nearly 100 patents and published over 10 papers in various journals and conferences.|ZHU Yu (zhuyu@fudan.edu.cn) received his BE degree (Hons.) in electronics engineering and ME degree (Hons.) in communication and information engineering from the University of Science and Technology of China in 1999 and 2002, respectively, and got his PhD degree in electrical and electronic engineering from the Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology, China in 2007. Since 2008, he has been with Fudan University, China, where he is currently a professor with the School of Information Science and Technology. His current research interests include broadband wireless communication systems and networks and signal processing for communications. He has served as an editor for the IEEE Wireless Communications Letters, and as an editor of the Journal of Communications and Information Networks.
Supported by:
ZTE Industry-University-Institute Cooperation Funds, the Natural Science Foundation of Shanghai(23ZR1407300);the National Natural Science Foundation of China(61771147)

摘要/Abstract

Abstract:

Hybrid beamforming (HBF) has become an attractive and important technology in massive multiple-input multiple-output (MIMO) millimeter-wave (mmWave) systems. There are different hybrid architectures in HBF depending on different connection strategies of the phase shifter network between antennas and radio frequency chains. This paper investigates HBF optimization with different hybrid architectures in broadband point-to-point mmWave MIMO systems. The joint hybrid architecture and beamforming optimization problem is divided into two sub-problems. First, we transform the spectral efficiency maximization problem into an equivalent weighted mean squared error minimization problem, and propose an algorithm based on the manifold optimization method for the hybrid beamformer with a fixed hybrid architecture. The overlapped subarray architecture which balances well between hardware costs and system performance is investigated. We further propose an algorithm to dynamically partition antenna subarrays and combine it with the HBF optimization algorithm. Simulation results are presented to demonstrate the performance improvement of our proposed algorithms.

Key words: hybrid beamforming, hybrid architecture, weighted mean square error, manifold optimization, dynamic subarrays

. [J]. ZTE Communications, 2023, 21(3): 93-104.

TANG Yuanqi, ZHANG Huimin, ZHENG Zheng, LI Ping, ZHU Yu. Hybrid Architecture and Beamforming Optimization for Millimeter Wave Systems[J]. ZTE Communications, 2023, 21(3): 93-104.

图/表 9

Figure 1 Downlink single-user mmWave multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) system with hybrid beamforming (HBF)

Figure 2 Diagram of four hybrid architectures

Table 1 Definitions of some parameters in the clustered delay line (CDL) channel model

Parameter	Definition
$P n$	Power of the $n$ -th cluster
$F r x, u, ? F t x, s$	Radiation patterns of the receiving and the transmitting antennas
$Φ n, m$	Random initial phases of different polarization combinations
$K n, m$	Cross polarization power ratio for the $m$ -th ray in the $n$ -th cluster
$λ 0$	Carrier wavelength
$r r x, n, m, r t x, n, m$	Spherical unit vectors of the receiving and the transmitting antennas
$v n, m$	Velocity vector

Table 1 Definitions of some parameters in the clustered delay line (CDL) channel model

Parameter	Definition
$P n$	Power of the $n$ -th cluster
$F r x, u, ? F t x, s$	Radiation patterns of the receiving and the transmitting antennas
$Φ n, m$	Random initial phases of different polarization combinations
$K n, m$	Cross polarization power ratio for the $m$ -th ray in the $n$ -th cluster
$λ 0$	Carrier wavelength
$r r x, n, m, r t x, n, m$	Spherical unit vectors of the receiving and the transmitting antennas
$v n, m$	Velocity vector

Figure 3 Diagram of three types of the overlapped subarray-connected architecture at the transmitter with Nt =512, NRFt=4

Figure 4 Spectral efficiency vs SNR for the hybrid beamforming (HBF-WMO) algorithm with different fixed hybrid architectures for a massive multiple-input multiple-output-orthogonal frequency division multiplexing (MIMO-OFDM) system with N=64, Nt =512, Nr =8, NRFt=4,?NRFr=2,Ns =2

Figure 5 Spectral efficiency vs number of transmit antennas for the HBF-WMO algorithm with different fixed hybrid architectures for a massive multiple-input multiple-output-orthogonal frequency division multiplexing (MIMO-OFDM) system with SNR=0 dB, N=64, Nr =8, NRFt=NRFr=2,Ns =2

Figure 6 Spectral efficiency vs number of transmit RF chains for the HBF-WMO algorithm with different fixed hybrid architectures for a massive multiple-input multiple-output-orthogonal frequency division multiplexing (MIMO-OFDM) system with SNR=0 dB, N=64, Nt =512, Nr =8, NRFt=2, Ns =2

Figure 7 Diagram of four fixed subarray types with the partially-connected architecture at the transmitter with Nt =512, NRFt=4

Figure 8 Spectral efficiency vs SNR for the HBF-WMO algorithm with different partitions of subarrays for a massive multiple-input multiple-output-orthogonal frequency division multiplexing (MIMO-OFDM) system with N=64, N t =512, N r =8, NRFt=4,?NRFr=2,Ns =2

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