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

## TL;DR

This paper introduces a new algorithm for detecting active users and estimating channels in 6G networks, improving efficiency in massive random access scenarios.

## Contribution

The novel CHMP algorithm uses a channel prior model with three state variables and BFE minimization for adaptive AUDCE.

## Key findings

- The CHMP algorithm accurately detects active users in wideband systems.
- It achieves precise channel estimation without prior knowledge of sparsity or channel priors.
- Simulation results validate the effectiveness of the proposed method.

## 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.