Iterative Detection and Decoding Schemes with LLR Refinements in Cell-Free Massive MIMO Networks

TL;DR

This paper introduces low-complexity detection and LLR refinement methods for coded cell-free massive MIMO systems, enhancing iterative detection and decoding with new schemes and analytical expressions.

Contribution

It proposes novel LLR processing schemes and derives closed-form expressions for local detectors, improving performance in cell-free massive MIMO networks.

Findings

LLR refinement techniques improve detection accuracy.

Proposed methods outperform existing approaches in simulations.

Analytical expressions facilitate efficient detector design.

Abstract

In this paper, we propose low-complexity local detectors and log-likelihood ratio (LLR) refinement techniques for a coded cell-free massive multiple input multiple output (CF- mMIMO) systems, where an iterative detection and decoding (IDD) scheme is applied using parallel interference cancellation (PIC) and access point (AP) selection. In particular, we propose three LLR processing schemes based on the individual processing of the LLRs of each AP, LLR censoring, and a linear combination of LLRs by assuming statistical independence. We derive new closed-form expressions for the local soft minimum mean square error (MMSE)-PIC detector and receive matched filter (RMF). We also examine the system performance as the number of iterations increases. Simulations assess the performance of the proposed techniques against existing approaches.