Dynamic Message Scheduling With Activity-Aware Residual Belief Propagation for Asynchronous mMTC Systems

TL;DR

This paper introduces a novel message-scheduling algorithm for asynchronous mMTC systems that improves device detection and channel estimation efficiency by leveraging activity-aware residual belief propagation.

Contribution

It proposes the MSGAMP algorithm with activity-aware scheduling, enhancing performance and reducing complexity in joint device detection and channel estimation.

Findings

Lower activity error rate compared to existing methods

Fewer iterations needed for convergence

Reduced computational complexity

Abstract

In this letter, we propose a joint active device detection and channel estimation framework based on factor graphs for asynchronous uplink grant-free massive multiple-antenna systems. We then develop the message-scheduling GAMP (MSGAMP) algorithm to perform joint active device detection and channel estimation. In MSGAMP we apply scheduling techniques based on the residual belief propagation (RBP) and the activity user detection (AUD) in which messages are generated using the latest available information. MSGAMP-type schemes show a good performance in terms of activity error rate and normalized mean squared error, requiring a smaller number of iterations for convergence and lower complexity than state-of-the-art techniques.