mlsanityData to 14 Oct 2026

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

arXiv 2212.10299 ↗First posted 20 Dec 2022eess.SY cs.LG cs.SYcited by 0

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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.

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References · 7

  1. 2021Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvementcited by 118
  2. 2018Maximizing acquisition functions for Bayesian optimizationcited by 114
  3. 2020Optimizing Coverage and Capacity in Cellular Networks using Machine Learningcited by 21
  4. 2020Reinforcement Learning for Efficient and Tuning-Free Link Adaptationcited by 7
  5. 2021Optimizing High-Dimensional Physics Simulations via Composite Bayesian Optimizationcited by 6
  6. 2020Deep Reinforcement Learning for Joint Spectrum and Power Allocation in Cellular Networkscited by 5
  7. 2021A Survey on User-Centric Cell-Free Massive MIMO Systemscited by 3

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