Clustering-Based Activity Detection Algorithms for Grant-Free Random Access in Cell-Free Massive MIMO

TL;DR

This paper proposes clustering-based algorithms for device activity detection in grant-free random access within cell-free massive MIMO networks, demonstrating improved performance over co-located architectures through macro-diversity gains.

Contribution

It introduces novel activity detection algorithms tailored for cell-free massive MIMO, leveraging distributed arrays and macro-diversity for enhanced detection accuracy.

Findings

Cell-free architecture improves activity detection performance.

Distributed arrays provide macro-diversity gains.

Algorithms outperform co-located architectures in large coverage areas.

Abstract

Future wireless networks need to support massive machine type communication (mMTC) where a massive number of devices accesses the network and massive MIMO is a promising enabling technology. Massive access schemes have been studied for co-located massive MIMO arrays. In this paper, we investigate the activity detection in grant-free random access for mMTC in cell-free massive MIMO networks using distributed arrays. Each active device transmits a non-orthogonal pilot sequence to the access points (APs) and the APs send the received signals to a central processing unit (CPU) for joint activity detection. The maximum likelihood device activity detection problem is formulated and algorithms for activity detection in cell-free massive MIMO are provided to solve it. The simulation results show that the macro-diversity gain provided by the cell-free architecture improves the activity detection performance compared to co-located architecture when the coverage area is large.