Work

Activity Detection and Channel Estimation Based on Correlated Hybrid Message Passing for Grant-Free Massive Random Access

Year: 2025

Type: article

Abstract: Massive machine-type communications (mMTC) in future 6G networks will involve a vast number of devices with sporadic traffic. Grant-free access has emerged as an effective strategy to reduce the access latency and processing overhead by allowing devices to transmit without prior permission, making accurate active user detection and channel estimation (AUDCE) crucial. In this paper, we investigate the joint AUDCE problem in wideband massive access systems. We develop an innovative channel prior model that captures the dual correlation structure of the channel using three state variables: active indication, channel supports, and channel values. By integrating Markov chains with coupled Gaussian distributions, the model effectively describes both the structural and numerical dependencies within the channel. We propose the correlated hybrid message passing (CHMP) algorithm based on Bethe free energy (BFE) minimization, which adaptively updates model parameters without requiring prior knowledge of user sparsity or channel priors. Simulation results show that the CHMP algorithm accurately detects active users and achieves precise channel estimation.

Source: Entropy

DOI: 10.3390/e27111111

Authors: Xiaofeng Liu, Xinrui Gong, Xiao Fu

Institutions: Yancheng Teachers University, Purple Mountain Laboratories, Southeast University

Language: English

Topic: IoT Networks and Protocols

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funder: China Postdoctoral Science Foundation