Cooperative Multi-Target Positioning for Cell-Free Massive MIMO with Multi-Agent Reinforcement Learning

TL;DR

This paper introduces a cooperative multi-agent reinforcement learning framework for cell-free massive MIMO positioning, combining signal processing and AP cooperation to enhance accuracy while reducing computational complexity.

Contribution

It proposes a novel joint positioning and correction framework using MARL and a cooperative weighted KNN scheme for improved accuracy and efficiency in cell-free massive MIMO systems.

Findings

MARL-based scheme outperforms traditional methods in accuracy

Co-WKNN effectively reduces bias from remote APs

Proposed architecture balances performance and computational load

Abstract

Cell-free massive multiple-input multiple-output (mMIMO) is a promising technology to empower next-generation mobile communication networks. In this paper, to address the computational complexity associated with conventional fingerprint positioning, we consider a novel cooperative positioning architecture that involves certain relevant access points (APs) to establish positioning similarity coefficients. Then, we propose an innovative joint positioning and correction framework employing multi-agent reinforcement learning (MARL) to tackle the challenges of high-dimensional sophisticated signal processing, which mainly leverages on the received signal strength information for preliminary positioning, supplemented by the angle of arrival information to refine the initial position estimation. Moreover, to mitigate the bias effects originating from remote APs, we design a cooperative weighted K-nearest neighbor (Co-WKNN)-based estimation scheme to select APs with a high correlation to participate in user positioning. In the numerical results, we present comparisons of various user positioning schemes, which reveal that the proposed MARL-based positioning scheme with Co-WKNN can effectively improve positioning performance. It is important to note that the cooperative positioning architecture is a critical element in striking a balance between positioning performance and computational complexity.