Distributed Iterative ML and Message Passing for Grant-Free Cell-Free Massive MIMO Systems

2025-07-28

AI Summary

To address the challenges of device activity detection, severe pilot contamination, and poor convergence of distributed algorithms in grant-free cell-free massive MIMO systems, this paper proposes a distributed signal processing framework jointly performing activity detection and channel estimation. The key contributions are: (1) a pseudo-prior hybrid variational Bayesian–expectation propagation (VB-EP) algorithm that significantly accelerates convergence and reduces sensitivity to initialization; (2) a component-wise iterative distributed maximum-likelihood method that jointly processes pilots and data symbols, ensuring stable convergence under finite-alphabet inputs; and (3) a fully distributed implementation enabled by a message-passing mechanism. Experiments demonstrate that the proposed approach effectively mitigates pilot contamination while substantially improving detection accuracy and system robustness, making it well-suited for massive-device, low-latency access scenarios.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Machine Learning: Active Learning
• Search and Optimization: Distributed Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Security and Privacy: Large-scale security measurements

Abstract

Cell-Free (CF) Massive Multiple-Input Multiple-Output (MaMIMO) is considered one of the leading candidates for enabling next-generation wireless communication. With the growing interest in the Internet of Things (IoT), the Grant-Free (GF) access scheme has emerged as a promising solution to support massive device connectivity. The integration of GF and CF-MaMIMO introduces significant challenges, particularly in designing distributed algorithms for activity detection and pilot contamination mitigation. In this paper, we propose a distributed algorithm that addresses these challenges. Our method first employs a component-wise iterative distributed Maximum Likelihood (ML) approach for activity detection, which considers both the pilot and data portions of the received signal. This is followed by a Pseudo-Prior Hybrid Variational Bayes and Expectation Propagation (PP-VB-EP) algorithm for joint data detection and channel estimation. Compared to conventional VB-EP, the proposed PP-VB-EP demonstrates improved convergence behavior and reduced sensitivity to initialization, especially when data symbols are drawn from a finite alphabet. The pseudo prior used in PP-VB-EP acts as an approximated posterior and serves as a regularization term that prevents the Message Passing (MP) algorithm from diverging. To compute the pseudo prior in a distributed fashion, we further develop a distributed version of the Variable-Level Expectation Propagation (VL-EP) algorithm.

Problem

• Distributed activity detection in GF CF-MaMIMO systems
• Mitigating pilot contamination in distributed ML algorithms
• Joint data detection and channel estimation via PP-VB-EP

Innovation

• Distributed ML for activity detection
• PP-VB-EP for joint data detection
• Distributed VL-EP for pseudo prior