Cell-Free Data Power Control Via Scalable Multi-Objective Bayesian Optimisation
This work addresses radio resource management for cell-free networks, which could improve service quality and resource utilization, but it appears incremental as it builds on existing optimization engines.
The paper tackles the problem of maximizing overall ergodic spectral efficiency for uplink-downlink data power control in cell-free networks by applying scalable multi-objective Bayesian optimization to address convergence-time limitations in large networks.
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.