English

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

Systems and Control 2023-01-18 v1 Machine Learning Systems and Control Applications Machine Learning

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.

Keywords

Cite

@article{arxiv.2212.10299,
  title  = {Cell-Free Data Power Control Via Scalable Multi-Objective Bayesian Optimisation},
  author = {Sergey S. Tambovskiy and Gábor Fodor and Hugo Tullberg},
  journal= {arXiv preprint arXiv:2212.10299},
  year   = {2023}
}

Comments

2022 IEEE 33rd Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

R2 v1 2026-06-28T07:44:42.559Z