Massive MIMO-OFDM Systems with Low Resolution ADCs: Cramér-Rao Bound, Sparse Channel Estimation, and Soft Symbol Decoding

Abstract

We consider the delay-domain sparse channel estimation and data detection/decoding problems in a massive multiple-input-multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) wireless communication system with low-resolution analog-to-digital converters (ADCs). The high non-linear distortion due to coarse quantization leads to severe performance degradation in conventional OFDM receivers, which necessitates novel receiver techniques. Firstly, we derive the Bayesian Cramr-Rao-lower-bound (CRLB) on the mean squared error (MSE) in recovering jointly compressible vectors from quantized noisy underdetermined measurements. Secondly, we formulate the pilot-assisted channel estimation as a multiple measurement vector (MMV) sparse recovery problem, and propose a variational Bayes (VB) algorithm to infer the posterior distribution of the channel. We benchmark the MSE performance of our algorithm with that of the CRLB, and numerically show that the VB algorithm meets the CRLB. Thirdly, we propose a soft symbol decoding algorithm that infers the posterior distributions of the data symbols given the quantized observations. We utilize the posterior statistics of the detected data symbols as virtual pilots, and propose an iterative soft symbol decoding and data-aided channel estimation procedure. Finally, we propose a variant of the iterative algorithm that utilizes the output bit log-likelihood ratios of the channel decoder to adapt the data prior to further improve the performance. We provide interesting insights into the impact of the various system parameters on the MSE and bit error rate of the proposed algorithms, and benchmark them against the state-of-the-art.