Decentralized Channel Estimation for the Uplink of Grant-Free Massive Machine-Type Communications

• Published in: IEEE transactions on communications 2022-02, Vol.70 (2), p.967-979
• Main Authors: Liu, Songbin, Zhang, Haochuan, Zou, Qiuyun
• Format: Article
• Language: English
• Subjects: Algorithms; Base stations; Channel estimation; Communications systems; decentralized message passing; Estimation; Evolutionary algorithms; Inference; Inference algorithms; Iterative methods; Joint channel and activity estimation; low-resolution quantization; Message passing; multiple-measurement vector; non-iid signature matrix; Quantization (signal); Robustness (mathematics); Uplink; Uplinking
• ISSN: 0090-6778; 1558-0857
• DOI: 10.1109/TCOMM.2021.3126619

This paper studies the joint estimation of channel fading and user activity for the uplink of a grant-free massive machine-type communication system. Comparing with previous studies, we consider more practical aspects of the system, including non-i.i.d. signature matrices, low-resolution quantization, and random users activated by an unknown sparse rate. A new estimation algorithm, termed hybrid decentralized generalized expectation consistent (HyDeGEC), is then derived based on a hybrid network that applies scalar message passing for the prior inference and vector message passing for the likelihood inference. This new algorithm outperforms many state-of-the-art techniques in terms of robustness (to non-i.i.d. signatures), complexity (in computation per iteration), and/or estimation accuracy (of the channel and the activity rate). The state evolution of the algorithm is also analyzed, which, as validated by simulations, can capture precisely the algorithm's per-iteration behavior in MSE. Summing up, the algorithm we propose here is practically effective, computationally efficient, and theoretically analyzable.