Distributed Joint User Activity Detection, Channel Estimation, and Data Detection via Expectation Propagation in Cell-Free Massive MIMO
Christian Forsch, Alexander Karataev, Laura Cottatellucci
TL;DR
This work tackles joint activity detection, channel estimation, and data detection (JACD) in grant-free cell-free massive MIMO by introducing JACD-EP, an expectation propagation-based algorithm operated on a factor graph. Activity, channel, and data are decoupled through auxiliary variables, allowing distributed AP processing with minimal fronthaul load and pilot-contamination mitigation via pilot-aware covariance adjustments. The approach combines categorical priors for activity/data and Gaussian models for channels, supported by EP message-passing rules and a pilot-contamination correction. Results show JACD-EP outperforms centralized MMSE and GaBP baselines across DER, NMSE, and SER, highlighting its practicality for scalable, low-latency IoT-enabled 6G networks.
Abstract
We consider the uplink of a grant-free cell-free massive multiple-input multiple-output (GF-CF-MaMIMO) system. We propose an algorithm for distributed joint activity detection, channel estimation, and data detection (JACD) based on expectation propagation (EP) called JACD-EP. We develop the algorithm by factorizing the a posteriori probability (APP) of activities, channels, and transmitted data, then, mapping functions and variables onto a factor graph, and finally, performing a message passing on the resulting factor graph. If users with the same pilot sequence are sufficiently distant from each other, the JACD-EP algorithm is able to mitigate the effects of pilot contamination which naturally occurs in grant-free systems due to the large number of potential users and limited signaling resources. Furthermore, it outperforms state-of-the-art algorithms for JACD in GF-CF-MaMIMO systems.