Novel Receiver Designs for Low-Resolution ADC-Based Uplink MU-MIMO Systems With Unknown Channel Covariance Information

Abstract

It is known that joint channel estimation and soft symbol decoding enhance the performance of multiple-input multiple-output (MIMO) communication systems with low-resolution analog-to-digital converters (ADCs). However, existing techniques require accurate knowledge of channel statistics to be effective. In this paper, we consider an uplink multi-user MIMO system and develop novel algorithms for joint estimation of channel coefficients and statistics along with soft symbol decoding. Specifically, we develop a sequential processing technique, based on variational Bayesian inference, for jointly estimating the covariance matrices of all users’ channels, the corresponding channel matrices, and soft symbols. We also develop an alternative block processing scheme for the same problem. Corresponding algorithms for sequential and block processing of unquantized data can be obtained as simplifications of our algorithms. On the analytical side, we derive a marginalized Bayesian Cramér-Rao Lower Bound (MB-CRLB) for covariance matrix estimation and elucidate the effect of the number of snapshots and SNR on the estimation error. Finally, we empirically evaluate the new algorithms and demonstrate their superior performance in scenarios where the channel statistics are unknown. The results also show that the mean squared error in channel covariance matrix estimation via the proposed algorithm closely follows the behavior of the MB-CRLB as a function of the SNR and number of frames.