A Linear Bayesian Learning Receiver Scheme for Massive MIMO Systems

TL;DR

This paper introduces a linear Bayesian learning (LBL) receiver for massive MIMO systems that reduces latency and improves detection reliability by combining Bayesian concepts with a parallel interference cancellation scheme.

Contribution

It proposes a novel iterative M-MIMO receiver using Bayesian learning and PIC, avoiding matrix inversion and significantly enhancing performance over existing MMSE and Bayesian receivers.

Findings

Outperforms MMSE and Bayesian receivers by at least 2 dB in BER.

Reduces latency processing time by 19 times.

Achieves better detection reliability in massive MIMO systems.

Abstract

Much stringent reliability and processing latency requirements in ultra-reliable-low-latency-communication (URLLC) traffic make the design of linear massive multiple-input-multiple-output (M-MIMO) receivers becomes very challenging. Recently, Bayesian concept has been used to increase the detection reliability in minimum-mean-square-error (MMSE) linear receivers. However, the latency processing time is a major concern due to the exponential complexity of matrix inversion operations in MMSE schemes. This paper proposes an iterative M-MIMO receiver that is developed by using a Bayesian concept and a parallel interference cancellation (PIC) scheme, referred to as a linear Bayesian learning (LBL) receiver. PIC has a linear complexity as it uses a combination of maximum ratio combining (MRC) and decision statistic combining (DSC) schemes to avoid matrix inversion operations. Simulation results show that the bit-error-rate (BER) and latency processing performances of the proposed receiver outperform the ones of MMSE and best Bayesian-based receivers by minimum $2$ dB and $19$ times for various M-MIMO system configurations.