CSI Calibration for Precoding in mmWave Massive MIMO Downlink Transmission Using Sparse Channel Prediction

IEEE Access

Jia-Chin Lin¹  Zhaocheng Yang²  Changwei Lv³ 

[1] Department of Communication Engineering, National Central University, Taoyuan City, Taiwan;Guangdong Key Laboratory of Intelligent Information Processing, College of Electronics and Information Engineering, Shenzhen University, Shenzhen, China;School of Sino-German Robotics, Shenzhen Institute of Information Technology, Shenzhen, China;

关键词: Channel prediction;    CSI calibration;    massive MIMO;    millimeter wave;    outdated CSI;    precoding;   

DOI  :  10.1109/ACCESS.2020.3017787

来源: DOAJ

【 摘 要 】

The channel state information (CSI) obtained from channel estimation will be outdated quickly in the millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems employing time-division duplex (TDD) setting, which results in significant performance degradation for the precoding and coherent signal detection. In order to overcome the CSI delay problem, this article proposes a novel downlink transmission scheme for the mmWave massive MIMO systems. In the proposed scheme, the base station (BS) estimates the channel coefficients by using the uplink pilots, and calibrates the CSI by employing an enhanced predictor which exploits the channel sparsity in both the angle and the time domains, followed by the interpolation to obtain the channel coefficients at the data rate. Then the signal radiated from the BS array is precoded by using the predicted channel coefficients so that the propagated signal can be added coherently and detected at the terminal. Simulation results show that the proposed scheme can overcome the CSI delay problem effectively, and improve the signal detection performance. We show that for system with 125 Hz Doppler frequency shift and 0.96 ms time slot, the uncoded bit error rate (BER) is improved from 2.4 × 10^-2 to 2.5 × 10^-3 by using our proposed method when the noise power ratio (SNR) is 10 dB.

【 授权许可】

Unknown