Joint Channel Estimation, Activity Detection and Decoding using Dynamic Message-Scheduling for Machine-Type Communications

TL;DR

This paper introduces BiMSGAMP, a novel message-passing algorithm with dynamic scheduling for joint channel estimation, activity detection, and data decoding in massive machine-type communications, improving performance over existing methods.

Contribution

The paper proposes BiMSGAMP, a bilinear message-scheduling GAMP algorithm that integrates channel and activity detection with decoding, using dynamic message scheduling strategies.

Findings

BiMSGAMP outperforms state-of-the-art algorithms in simulations.

Dynamic scheduling improves convergence and accuracy.

Inclusion of channel decoding enhances overall system performance.

Abstract

In this work, we present a joint channel estimation, activity detection and data decoding scheme for massive machine-type communications. By including the channel and the a priori activity factor in the factor graph, we present the bilinear message-scheduling GAMP (BiMSGAMP), a message-passing solution that uses the channel decoder beliefs to refine the activity detection and data decoding. We include two message-scheduling strategies based on the residual belief propagation and the activity user detection in which messages are evaluated and scheduled in every new iteration. An analysis of the convergence of BiMSGAMP along with a study of its computational complexity is carried out. Numerical results show that BiMSGAMP outperforms state-of-the-art algorithms, highlighting the gains achieved by using the dynamic scheduling strategies and the effects of the channel decoding part in the system.