Robust Beamforming Under Channel Prediction Errors for Time-Varying MIMO System

doi:10.12142/ZTECOM.202303011

ZTE Communications ›› 2023, Vol. 21 ›› Issue (3): 77-85.DOI: 10.12142/ZTECOM.202303011

收稿日期:2022-12-23 出版日期:2023-09-21 发布日期:2023-03-22

Robust Beamforming Under Channel Prediction Errors for Time-Varying MIMO System

ZHU Yuting¹, LI Zeng^2,³(), ZHANG Hongtao¹

^1.Key Lab of Universal Wireless Communications, Ministry of Education of China, Beijing University of Posts and Telecommunications, Beijing 100876, China
^2.ZTE Corporation, Shenzhen 518057, China
^3.State Key Laboratory of Mobile Network and Mobile Multimedia Technology, Shenzhen 518055, China

Received:2022-12-23 Online:2023-09-21 Published:2023-03-22
About author:ZHU Yuting received her bachelor’s degree in communication engineering from Beijing University of Posts and Telecommunications (BUPT), China in 2022. She is currently working toward a master’s degree in communication and information engineering at the School of Artificial Intelligence, BUPT. Her research interests include the emerging technologies of 5G wireless communication networks.|LI Zeng (li.zeng@zte.com.cn) received his master’s degree in information and communication engineering from Harbin Institute of Technology, China in 2016. He is currently working at the Algorithm Department, Wireless Product R&D Institute, Wireless Product Operation Division, ZTE Corporation. His research interests include 5G wireless communications and signal processing.|ZHANG Hongtao received his PhD degree in communication and information systems from Beijing University of Posts and Telecommunications (BUPT), China in 2008. He is currently a full professor with BUPT. He has authored or co-authored more than 100 articles on international journals and conferences, and has filed more than 50 patents. He is also the author of ten technical books. His research interests include 5G wireless communications and signal processing.
Supported by:
the ZTE Industry‐University‐Institute Cooperation Funds;2021ZTE01‐03

摘要/Abstract

Abstract:

The accuracy of acquired channel state information (CSI) for beamforming design is essential for achievable performance in multiple-input multiple-output (MIMO) systems. However, in a high-speed moving scene with time-division duplex (TDD) mode, the acquired CSI depending on the channel reciprocity is inevitably outdated, leading to outdated beamforming design and then performance degradation. In this paper, a robust beamforming design under channel prediction errors is proposed for a time-varying MIMO system to combat the degradation further, based on the channel prediction technique. Specifically, the statistical characteristics of historical channel prediction errors are exploited and modeled. Moreover, to deal with random error terms, deterministic equivalents are adopted to further explore potential beamforming gain through the statistical information and ultimately derive the robust design aiming at maximizing weighted sum-rate performance. Simulation results show that the proposed beamforming design can maintain outperformance during the downlink transmission time even when channels vary fast, compared with the traditional beamforming design.

Key words: time-varying channels, time-division duplex, robust beamforming, channel prediction errors, weighted sum-rate maximization

. [J]. ZTE Communications, 2023, 21(3): 77-85.

ZHU Yuting, LI Zeng, ZHANG Hongtao. Robust Beamforming Under Channel Prediction Errors for Time-Varying MIMO System[J]. ZTE Communications, 2023, 21(3): 77-85.

图/表 8

Figure 1 Illustration of a time-division duplex (TDD) multi-user multiple-input multiple-output (MU-MIMO) system where v denotes the speed of mobile UE

Figure 2 System model for proposed beamforming scheme

Figure 3 Channel prediction error model and time slot structure

Figure 4 Convergence behavior of the proposed beamforming design and WMMSE

Figure 5 Averaged sum rate versus SNR under different beamforming methods with v=60 km/h

Figure 6 Averaged sum rate versus SNR under different beamforming methods with v=120 km/h

Figure 7 Sum rate versus DL time slots under different beamforming methods with v=60 km/h

Figure 8 Sum rate versus DL time slots under different beamforming methods with v=60 km/h

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