Cell-Free Data Power Control Via Scalable Multi-Objective Bayesian Optimisation

TL;DR

This paper introduces a scalable multi-objective Bayesian optimisation approach to enhance data power control in cell-free networks, aiming to maximize spectral efficiency efficiently in large-scale systems.

Contribution

It proposes a novel application of multi-objective Bayesian optimisation combined with multi-fidelity emulation for radio resource management in cell-free networks.

Findings

Improved spectral efficiency in large cell-free networks.

Faster convergence times compared to traditional methods.

Effective handling of multi-objective optimisation in complex systems.

Abstract

Cell-free multi-user multiple input multiple output networks are a promising alternative to classical cellular architectures, since they have the potential to provide uniform service quality and high resource utilisation over the entire coverage area of the network. To realise this potential, previous works have developed radio resource management mechanisms using various optimisation engines. In this work, we consider the problem of overall ergodic spectral efficiency maximisation in the context of uplink-downlink data power control in cell-free networks. To solve this problem in large networks, and to address convergence-time limitations, we apply scalable multi-objective Bayesian optimisation. Furthermore, we discuss how an intersection of multi-fidelity emulation and Bayesian optimisation can improve radio resource management in cell-free networks.