Unfolding for Joint Channel Estimation and Symbol Detection in MIMO Communication Systems

2023/08/17 by Bhattacharya, Swati, Hari, K. V. S., Eldar, Yonina C.
#FOS: Computer and information sciences #FOS: Electrical engineering #Information Theory (cs.IT) #Signal Processing (eess.SP) #electronic engineering #information engineering

paper · doi:10.48550/arxiv.2308.08917

Abstract

This paper proposes a Joint Channel Estimation and Symbol Detection (JED) scheme for Multiple-Input Multiple-Output (MIMO) wireless communication systems. Our proposed method for JED using Alternating Direction Method of Multipliers (JED-ADMM) and its model-based neural network version JED using Unfolded ADMM (JED-U-ADMM) markedly improve the symbol detection performance over JED using Alternating Minimization (JED-AM) for a range of MIMO antenna configurations. Both proposed algorithms exploit the non-smooth constraint, that occurs as a result of the Quadrature Amplitude Modulation (QAM) data symbols, to effectively improve the performance using the ADMM iterations. The proposed unfolded network JED-U-ADMM consists of a few trainable parameters and requires a small training set. We show the efficacy of the proposed methods for both uncorrelated and correlated MIMO channels. For certain configurations, the gain in SNR for a desired BER of 10^-2 for the proposed JED-ADMM and JED-U-ADMM is upto 4 dB and is also accompanied by a significant reduction in computational complexity of upto 75%, depending on the MIMO configuration, as compared to the complexity of JED-AM.

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