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

arXiv: https://arxiv.org/abs/2212.10299 · HTML: https://mlsanity.com/paper/2212.10299/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 22 references that are in the map.
- First posted on arXiv 20 Dec 2022.

## Builds on (most cited first, 7 of 7)

- [Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvement](https://mlsanity.com/paper/2105.08195/) (2021)
- [Maximizing acquisition functions for Bayesian optimization](https://mlsanity.com/paper/1805.10196/) (2018)
- [Optimizing Coverage and Capacity in Cellular Networks using Machine Learning](https://mlsanity.com/paper/2010.13710/) (2020)
- [Reinforcement Learning for Efficient and Tuning-Free Link Adaptation](https://mlsanity.com/paper/2010.08651/) (2020)
- [Optimizing High-Dimensional Physics Simulations via Composite Bayesian Optimization](https://mlsanity.com/paper/2111.14911/) (2021)
- [Deep Reinforcement Learning for Joint Spectrum and Power Allocation in Cellular Networks](https://mlsanity.com/paper/2012.10682/) (2020)
- [A Survey on User-Centric Cell-Free Massive MIMO Systems](https://mlsanity.com/paper/2104.13667/) (2021)

## Abstract (arXiv)

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.