ML and MAP Device Activity Detections for Grant-Free Massive Access in Multi-Cell Networks

TL;DR

This paper develops advanced device activity detection methods for multi-cell networks in massive IoT scenarios, considering inter-cell interference and prior information, and proposes algorithms that outperform existing single-cell approaches.

Contribution

It introduces joint ML and MAP estimation techniques for device activity and interference detection in multi-cell networks, extending single-cell methods and incorporating cooperation and prior knowledge.

Findings

Joint ML and MAP estimations outperform non-cooperative methods.

Cooperative detection improves accuracy at higher backhaul and computational costs.

Proposed algorithms effectively handle inter-cell interference in massive IoT networks.

Abstract

Device activity detection is one main challenge in grant-free massive access, which is recently proposed to support massive machine-type communications (mMTC). Existing solutions for device activity detection fail to consider inter-cell interference generated by massive IoT devices or important prior information on device activities and inter-cell interference. In this paper, given different numbers of observations and network parameters, we consider both non-cooperative device activity detection and cooperative device activity detection in a multi-cell network, consisting of many access points (APs) and IoT devices. Under each activity detection mechanism, we consider the joint maximum likelihood (ML) estimation and joint maximum a posterior probability (MAP) estimation of both device activities and interference powers, utilizing tools from probability, stochastic geometry, and optimization. Each estimation problem is a challenging non-convex problem, and a coordinate descent algorithm is proposed to obtain a stationary point. Each proposed joint ML estimation extends the existing one for a single-cell network by considering the estimation of interference powers, together with the estimation of device activities. Each proposed joint MAP estimation further enhances the corresponding joint ML estimation by exploiting prior distributions of device activities and interference powers. The proposed joint ML estimation and joint MAP estimation under cooperative detection outperform the respective ones under non-cooperative detection at the costs of increasing backhaul burden, knowledge of network parameters, and computational complexities.