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

• Research Papers • Previous Articles Next Articles

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)

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

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.

Figures/Tables 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

References 26

1	PI Z Y, KHAN F. An introduction to millimeter-wave mobile broadband systems [J]. IEEE communications magazine, 2011, 49(6): 101–107. DOI: 10.1109/MCOM.2011.5783993 DOI URL
2	ROH W, SEOL J Y, PARK J, et al. Millimeter-wave beamforming as an enabling technology for 5G cellular communications: theoretical feasibility and prototype results [J]. IEEE communications magazine, 2014, 52(2): 106–113. DOI: 10.1109/MCOM.2014.6736750 DOI URL
3	RANGAN S, RAPPAPORT T S, ERKIP E. Millimeter-wave cellular wireless networks: potentials and challenges [J]. Proceedings of the IEEE, 2014, 102(3): 366–385. DOI: 10.1109/JPROC.2014.2299397 DOI URL
4	PAULRAJ A J, GORE D A, NABAR R U, et al. An overview of MIMO communications: a key to gigabit wireless [J]. Proceedings of the IEEE, 2004, 92(2): 198–218. DOI: 10.1109/JPROC.2003.821915 DOI URL
5	AKDENIZ M R, LIU Y P, SAMIMI M K, et al. Millimeter wave channel modeling and cellular capacity evaluation [J]. IEEE journal on selected areas in communications, 2014, 32(6): 1164–1179. DOI: 10.1109/JSAC.2014.2328154 DOI URL
6	ZHANG J, YU X H, LETAIEF K B. Hybrid beamforming for 5G and beyond millimeter-wave systems: a holistic view [J]. IEEE open journal of the communications society, 2019, 1: 77–91. DOI: 10.1109/OJCOMS.2019.2959595 DOI URL
7	LARSSON E G, EDFORS O, TUFVESSON F, et al. Massive MIMO for next generation wireless systems [J]. IEEE communications magazine, 2014, 52(2): 186–195. DOI: 10.1109/MCOM.2014.6736761 DOI URL
8	MOLISCH A F, RATNAM V V, HAN S Q, et al. Hybrid beamforming for massive MIMO: a survey [J]. IEEE communications magazine, 2017, 55(9): 134–141. DOI: 10.1109/MCOM.2017.1600400 DOI URL
9	AYACH O E, RAJAGOPAL S, ABU-SURRA S, et al. Spatially sparse precoding in millimeter wave MIMO systems [J]. IEEE transactions on wireless communications, 2014, 13(3): 1499–1513. DOI: 10.1109/TWC.2014.011714.130846 DOI URL
10	SOHRABI F, YU W. Hybrid analog and digital beamforming for mmWave OFDM large-scale antenna arrays [J]. IEEE journal on selected areas in communications, 2017, 35(7): 1432–1443. DOI: 10.1109/JSAC.2017.2698958 DOI URL
11	YU X H, SHEN J C, ZHANG J, et al. Alternating minimization algorithms for hybrid precoding in millimeter wave MIMO systems [J]. IEEE journal of selected topics in signal processing, 2016, 10(3): 485–500. DOI: 10.1109/JSTSP.2016.2523903 DOI URL
12	LIN T, CONG J Q, ZHU Y, et al. Hybrid beamforming for millimeter wave systems using the MMSE criterion [J]. IEEE transactions on communications, 2019, 67(5): 3693–3708. DOI: 10.1109/TCOMM.2019.2893632 DOI URL
13	ZHAO X Y, LIN T, ZHU Y, et al. Partially-connected hybrid beamforming for spectral efficiency maximization via a weighted MMSE equivalence [J]. IEEE transactions on wireless communications, 2021, 20(12): 8218–8232. DOI: 10.1109/TWC.2021.3091524 DOI URL
14	ZHANG D D, WANG Y F, LI X H, et al. Hybridly connected structure for hybrid beamforming in mmWave massive MIMO systems [J]. IEEE transactions on communications, 2018, 66(2): 662–674. DOI: 10.1109/TCOMM.2017.2756882 DOI URL
15	GAUTAM P R, ZHANG L. Hybrid precoding for partial-full mixed connection mmWave MIMO [C]//The IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2021: 271–275. DOI: 10.1109/SSP49050.2021.9513847 DOI URL
16	SONG N, YANG T, SUN H. Overlapped subarray based hybrid beamforming for millimeter wave multiuser massive MIMO [J]. IEEE signal processing letters, 2017, 24(5): 550–554. DOI: 10.1109/LSP.2017.2681689 DOI URL
17	CHEN Y, CHEN D, JIANG T, et al. Millimeter-wave massive MIMO systems relying on generalized sub-array-connected hybrid precoding [J]. IEEE transactions on vehicular technology, 2019, 68(9): 8940–8950. DOI: 10.1109/TVT.2019.2930639 DOI URL
18	MÉNDEZ-RIAL R, RUSU C, GONZÁLEZ-PRELCIC N, et al. Hybrid MIMO architectures for millimeter wave communications: phase shifters or switches? [J]. IEEE access, 2016, 4: 247–267. DOI: 10.1109/ACCESS.2015.2514261 DOI URL
19	ALKHATEEB A, NAM Y H, ZHANG J Z, et al. Massive MIMO combining with switches [J]. IEEE wireless communications letters, 2016, 5(3): 232–235. DOI: 10.1109/LWC.2016.2522963 DOI URL
20	PARK S, ALKHATEEB A, HEATH R W. Dynamic subarrays for hybrid precoding in wideband mmWave MIMO systems [J]. IEEE transactions on wireless communications, 2017, 16(5): 2907–2920. DOI: 10.1109/TWC.2017.2671869 DOI URL
21	JIN J N, XIAO C S, CHEN W, et al. Channel-statistics-based hybrid precoding for millimeter-wave MIMO systems with dynamic subarrays [J]. IEEE transactions on communications, 2019, 67(6): 3991–4003. DOI: 10.1109/TCOMM.2019.2899628 DOI URL
22	YAN L F, HAN C, YUAN J H. A dynamic array-of-subarrays architecture and hybrid precoding algorithms for terahertz wireless communications [J]. IEEE journal on selected areas in communications, 2020, 38(9): 2041–2056. DOI: 10.1109/JSAC.2020.3000876 DOI URL
23	3GPP. Study on channel model for frequencies from 0.5 to 100 GHz: TR 38.901 V16.2.0 [S]. 2020
24	LI H Y, LI M, LIU Q. Hybrid beamforming with dynamic subarrays and low-resolution PSs for mmWave MU-MISO systems [J]. IEEE transactions on communications, 2020, 68(1): 602–614. DOI: 10.1109/TCOMM.2019.2950905 DOI URL
25	CHEN Y, CHEN D, JIANG T, et al. Channel-covariance and angle-of-departure aided hybrid precoding for wideband multiuser millimeter wave MIMO systems [J]. IEEE transactions on communications, 2019, 67(12): 8315–8328. DOI: 10.1109/TCOMM.2019.2942307 DOI URL
26	CHEN Y, XIONG Y F, CHEN D, et al. Hybrid precoding for wideband millimeter wave MIMO systems in the face of beam squint [J]. IEEE transactions on wireless communications, 2021, 20(3): 1847–1860. DOI: 10.1109/TWC.2020.3036945 DOI URL

[1]	JI Yuhe, HAN Jing, ZHAO Yongxin, ZHANG Shenglin, GONG Zican. Log Anomaly Detection Through GPT-2 for Large Scale Systems [J]. ZTE Communications, 2023, 21(3): 70-76.
[2]	Shuangfeng Han, Chih-Lin I, Zhikun Xu, Qi Sun, Haibin Li. Energy-Efficient Large-Scale Antenna Systems with Hybrid Digital-Analog Beamforming Structure [J]. ZTE Communications, 2015, 13(1): 28-34.

Hybrid Architecture and Beamforming Optimization for Millimeter Wave Systems

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 9

References 26

Related Articles 2

Recommended Articles

Metrics