Access Point Deployment for Localizing Accuracy and User Rate in Cell-Free Systems

TL;DR

This paper presents a novel AP deployment method for cell-free systems that balances sensing accuracy and user rate using an integrated metric and reinforcement learning, improving overall and minimum performance.

Contribution

It introduces a new combined metric for AP deployment in cell-free systems and applies SAC reinforcement learning to optimize placement, addressing high-dimensional non-convex problems.

Findings

Achieves 20% improvement in overall performance

Attains 120% enhancement in lowest performance

Effectively balances sensing accuracy and user rate

Abstract

Evolving next-generation mobile networks is designed to provide ubiquitous coverage and networked sensing. With utility of multi-view sensing and multi-node joint transmission, cell-free is a promising technique to realize this prospect. This paper aims to tackle the problem of access point (AP) deployment in cell-free systems to balance the sensing accuracy and user rate. By merging the D-optimality with Euclidean criterion, a novel integrated metric is proposed to be the objective function for both max-sum and max-min problems, which respectively guarantee the overall and lowest performance in multi-user communication and target tracking scenario. To solve the corresponding high dimensional non-convex multi-objective problem, the Soft actor-critic (SAC) is utilized to avoid risk of local optimal result. Numerical results demonstrate that proposed SAC-based APs deployment method achieves $20\%$ of overall performance and $120\%$ of lowest performance.