Covariance-Based Joint Device Activity and Delay Detection in Asynchronous mMTC

TL;DR

This paper introduces a covariance-based method for joint device activity and delay detection in asynchronous mMTC, outperforming compressed sensing approaches by leveraging multiple antennas for better detection accuracy.

Contribution

The paper proposes a novel covariance-based detection algorithm that improves performance over existing compressed sensing methods in asynchronous mMTC scenarios.

Findings

Significantly better detection performance than CS approaches.

Effective utilization of multiple antennas enhances detection accuracy.

Proposed algorithms efficiently solve the joint detection problem.

Abstract

In this letter, we study the joint device activity and delay detection problem in asynchronous massive machine-type communications (mMTC), where all active devices asynchronously transmit their preassigned preamble sequences to the base station (BS) for device identification and delay detection. We first formulate this joint detection problem as a maximum likelihood estimation problem, which depends on the received signal only through its sample covariance, and then propose efficient coordinate descent type of algorithms to solve the formulated problem. Our proposed covariance-based approach is sharply different from the existing compressed sensing (CS) approach for the same problem. Numerical results show that our proposed covariance-based approach significantly outperforms the CS approach in terms of the detection performance since our proposed approach can make better use of the BS antennas than the CS approach.