Study of Joint Activity Detection and Channel Estimation Based on Message Passing with RBP Scheduling for MTC

TL;DR

This paper introduces a message-scheduling GAMP algorithm based on RBP for joint device activity detection and channel estimation in massive MIMO systems, achieving high accuracy with low computational cost.

Contribution

It proposes a novel MSGAMP algorithm with RBP-based scheduling that improves efficiency and performance over existing methods in massive MIMO activity detection.

Findings

Low activity error rate achieved

Normalized mean squared error is minimized

Requires fewer iterations for convergence

Abstract

In this work, based on the hybrid generalized approximate message passing (HyGAMP) algorithm, we propose the message-scheduling GAMP (MSGAMP) algorithm in order to address the problem of joint active device detection and channel estimation in an uplink grant-free massive MIMO system scenario. In MSGAMP, we apply three different scheduling techniques based on the Residual Belief Propagation (RBP) in which messages are generated using the latest available information. With a much lower computational cost than the state-of-the-art algorithms, MSGAMP-type schemes exhibits good performance in terms of activity error rate and normalized mean squared error, requiring a small number of iterations for convergence. %