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

T. Ssettumba, Z. Shao, L. Landau, R. C. de Lamare

TL;DR

Addressing uplink detection in dense CF-mMIMO, the paper introduces an iterative detection and decoding (IDD) framework built on low-complexity local detectors (RMF and MMSE-PIC) with AP selection. It derives closed-form local soft-MMSE-PIC and RMF detectors, and proposes two LLR refinement schemes (LLR censoring and LLR combining) to boost BER performance while managing complexity. An IDD loop exchanges soft beliefs between detectors and LDPC decoders, and Box-plus SPA is employed to reduce computational load. Simulation results show that MMSE-PIC with LLR refinements achieves significant BER gains and supports scalable deployments through dynamic AP selection and decentralized processing.

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.

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

TL;DR

Addressing uplink detection in dense CF-mMIMO, the paper introduces an iterative detection and decoding (IDD) framework built on low-complexity local detectors (RMF and MMSE-PIC) with AP selection. It derives closed-form local soft-MMSE-PIC and RMF detectors, and proposes two LLR refinement schemes (LLR censoring and LLR combining) to boost BER performance while managing complexity. An IDD loop exchanges soft beliefs between detectors and LDPC decoders, and Box-plus SPA is employed to reduce computational load. Simulation results show that MMSE-PIC with LLR refinements achieves significant BER gains and supports scalable deployments through dynamic AP selection and decentralized processing.

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.
Paper Structure (16 sections, 25 equations, 2 figures, 1 table)

This paper contains 16 sections, 25 equations, 2 figures, 1 table.

Table of Contents

  1. Introduction
  2. Proposed System Model
  3. Uplink Pilot Transmission and Channel Estimation
  4. Iterative Local Receiver Design
  5. Access Point Selection Scheme
  6. Receive Matched Filter
  7. MMSE with Parallel Interference Cancellation
  8. Gaussian Approximation of the Soft Local Receive filters
  9. Computational Complexity
  10. Decoding Algorithm
  11. Proposed LLR Processing and Refinement Strategies
  12. Standard LLR Processing
  13. LLR Censoring
  14. LLR Combining
  15. Simulation Results
  16. ...and 1 more sections

Figures (2)

  • Figure 1: Block diagram of the proposed IDD scheme for decentralized CF-mMIMO.
  • Figure 2: BER versus SNR while comparing the studied detectors and LLR refinement strategies for $L=4$, $N=4$, $K=4$: (a) all APs (Full-Network), $\text{IDD}=2$ (b) LP-wAPS (Scalable), $\text{IDD}=2$ (c) Full-Network for MMSE-PIC, (d) LP-wAPS (Scalable) with MMSE-PIC.