Activity Detection in Distributed Massive MIMO With Pilot-Hopping and Activity Correlation

TL;DR

This paper explores how exploiting activity correlation among sensors in massive MIMO systems with pilot-hopping can significantly improve activity detection performance by incorporating regularizers into non-negative least squares algorithms.

Contribution

It introduces a method that leverages activity correlation through regularizers to enhance detection in grant-free random access systems with pilot-hopping.

Findings

Regularizers improve detection accuracy with correlated activities.

Exploiting activity correlation yields significant performance gains.

Method outperforms traditional non-negative least squares in correlated scenarios.

Abstract

Many real-world scenarios for massive machine-type communication involve sensors monitoring a physical phenomenon. As a consequence, the activity pattern of these sensors will be correlated. In this letter, we study how the correlation of user activities can be exploited to improve detection performance in grant-free random access systems where the users transmit pilot-hopping sequences and the detection is performed based on the received energy. We show that we can expect considerable performance gains by adding regularizers, which take the activity correlation into account, to the non-negative least squares, which has been shown to work well for independent user activity.