Dynamic Message Scheduling Based on Activity-Aware Residual Belief Propagation for Asynchronous mMTC

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.

Automated summary

The letter presents a joint active device detection and channel estimation framework based on factor graphs, and introduces the MSGAMP algorithm for this purpose. The MSGAMP algorithm shows good performance in terms of activity error rate and mean squared error, with fewer iterations and lower complexity compared to state-of-the-art techniques.