Decentralized and Centralized IDD Schemes for Cell-Free Networks

TL;DR

This paper introduces iterative interference cancellation and IDD schemes with access point selection for cell-free massive MIMO systems, demonstrating improved performance and reduced complexity through decentralized processing and LLR refinement strategies.

Contribution

It develops centralized and decentralized LMMSE filters with APs-Sel, a list-based detector, and a message-passing IDD scheme with LLR refinement, advancing interference management in CF-mMIMO.

Findings

Decentralized processing outperforms centralized in BER and complexity.

LLR refinement strategies enhance network performance.

Distributed architecture with LLR refinements shows superiority.

Abstract

In this paper, we propose iterative interference cancellation schemes with access points selection (APs-Sel) for cell-free massive multiple-input multiple-output (CF-mMIMO) systems. Closed-form expressions for centralized and decentralized linear minimum mean square error (LMMSE) receive filters with APs-Sel are derived assuming imperfect channel state information (CSI). Furthermore, we develop a list-based detector based on LMMSE receive filters that exploits interference cancellation and the constellation points. A message-passing-based iterative detection and decoding (IDD) scheme that employs low-density parity-check (LDPC) codes is then developed. Moreover, log-likelihood ratio (LLR) refinement strategies based on censoring and a linear combination of local LLRs are proposed to improve the network performance. We compare the cases with centralized and decentralized processing in terms of bit error rate (BER) performance, complexity, and signaling under perfect CSI (PCSI) and imperfect CSI (ICSI) and verify the superiority of the distributed architecture with LLR refinements.