A Bayesian Receiver with Improved Complexity-Reliability Trade-off in Massive MIMO Systems

TL;DR

This paper introduces a Bayesian iterative detector for massive MIMO systems that significantly improves the reliability-complexity trade-off, achieving high performance with lower complexity in 5G scenarios.

Contribution

It proposes a novel Bayesian iterative detection scheme combined with parallel interference cancellation and decision statistics, enhancing massive MIMO receiver efficiency.

Findings

Significant performance gain over existing low complexity detectors.

Achieves one order of magnitude lower complexity than stack successive cancellation decoding.

Effective integration with polar code decoding in 5G NR.

Abstract

The stringent requirements on reliability and processing delay in the fifth-generation ($5$G) cellular networks introduce considerable challenges in the design of massive multiple-input-multiple-output (M-MIMO) receivers. The two main components of an M-MIMO receiver are a detector and a decoder. To improve the trade-off between reliability and complexity, a Bayesian concept has been considered as a promising approach that enhances classical detectors, e.g. minimum-mean-square-error detector. This work proposes an iterative M-MIMO detector based on a Bayesian framework, a parallel interference cancellation scheme, and a decision statistics combining concept. We then develop a high performance M-MIMO receiver, integrating the proposed detector with a low complexity sequential decoding for polar codes. Simulation results of the proposed detector show a significant performance gain compared to other low complexity detectors. Furthermore, the proposed M-MIMO receiver with sequential decoding ensures one order magnitude lower complexity compared to a receiver with stack successive cancellation decoding for polar codes from the 5G New Radio standard.