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

arXiv: https://arxiv.org/abs/2110.14112 · HTML: https://mlsanity.com/paper/2110.14112/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2021.

## Built on by (most cited first, 2 of 2)

- [GCEPNet: Graph Convolution-Enhanced Expectation Propagation for Massive MIMO Detection](https://mlsanity.com/paper/2404.14886/) (2024)
- [Graph Neural Network Aided MU-MIMO Detectors](https://mlsanity.com/paper/2206.09381/) (2022)

## Abstract (arXiv)

The stringent requirements on reliability and processing delay in the fifth-generation (5G) 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.