Study of Delay-Calibrated Joint User Activity Detection, Channel Estimation and Data Detection for Asynchronous mMTC Systems

TL;DR

This paper introduces a delay-calibrated joint detection and estimation algorithm for asynchronous mMTC systems, leveraging oversampling and Bayesian inference to improve user activity detection, channel estimation, and data decoding.

Contribution

It proposes a novel delay-calibrated joint detection algorithm using expectation-maximization and approximate message passing for asynchronous mMTC systems.

Findings

Effective in reducing mean-squared errors of channel and data estimates

Improves detection probability and reduces misdetection rates

Utilizes oversampling to accurately estimate continuous delays

Abstract

This work considers uplink asynchronous massive machine-type communications, where a large number of low-power and low-cost devices asynchronously transmit short packets to an access point equipped with multiple receive antennas. If orthogonal preambles are employed, massive collisions will occur due to the limited number of orthogonal preambles given the preamble sequence length. To address this problem, we propose a delay-calibrated joint user activity detection, channel estimation, and data detection algorithm, and investigate the benefits of oversampling in estimating continuous-valued time delays at the receiver. The proposed algorithm is based on the expectation-maximization method, which alternately estimates the delays and detects active users and their channels and data by noting that the collided users have different delays. Under the Bayesian inference framework, we develop a computationally efficient iterative algorithm using the approximate message passing principle to resolve the joint user activity detection, channel estimation, and data detection problem. Numerical results demonstrate the effectiveness of the proposed algorithm in terms of the normalized mean-squared errors of channel and data symbols, and the probability of misdetection.