# Bayesian Optimization for Non-Cooperative Game-Based Radio Resource Management

arXiv: https://arxiv.org/abs/2512.01245 · HTML: https://mlsanity.com/paper/2512.01245/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 21 references that are in the map.
- First posted on arXiv 1 Dec 2025.

## Builds on (most cited first, 10 of 10)

- [A Tutorial on Bayesian Optimization](https://mlsanity.com/paper/1807.02811/) (2018)
- [On Kernelized Multi-armed Bandits](https://mlsanity.com/paper/1704.00445/) (2017)
- [Near-Optimal Reinforcement Learning with Self-Play](https://mlsanity.com/paper/2006.12007/) (2020)
- [Federated Bayesian Optimization via Thompson Sampling](https://mlsanity.com/paper/2010.10154/) (2020)
- [A Bayesian optimization approach to find Nash equilibria](https://mlsanity.com/paper/1611.02440/) (2016)
- [Bayesian and Multi-Armed Contextual Meta-Optimization for Efficient Wireless Radio Resource Management](https://mlsanity.com/paper/2301.06510/) (2023)
- [Safe and Accelerated Deep Reinforcement Learning-based O-RAN Slicing: A Hybrid Transfer Learning Approach](https://mlsanity.com/paper/2309.07265/) (2023)
- [Resource Allocation for Device-to-Device Communications Underlaying Heterogeneous Cellular Networks Using Coalitional Games](https://mlsanity.com/paper/1811.02350/) (2018)
- [Fair Beam Allocations through Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/2310.15911/) (2023)
- [Multi-Fidelity Bayesian Optimization for Nash Equilibria with Black-Box Utilities](https://mlsanity.com/paper/2505.11265/) (2025)

## Abstract (arXiv)

Radio resource management in modern cellular networks often calls for the optimization of complex utility functions that are potentially conflicting between different base stations (BSs). Coordinating the resource allocation strategies efficiently across BSs to ensure stable network service poses significant challenges, especially when each utility is accessible only via costly, black-box evaluations. This paper considers formulating the resource allocation among spectrum sharing BSs as a non-cooperative game, with the goal of aligning their allocation incentives toward a stable outcome. To address this challenge, we propose PPR-UCB, a novel Bayesian optimization (BO) strategy that learns from sequential decision-evaluation pairs to approximate pure Nash equilibrium (PNE) solutions. PPR-UCB applies martingale techniques to Gaussian process (GP) surrogates and constructs high probability confidence bounds for utilities uncertainty quantification. Experiments on downlink transmission power allocation in a multi-cell multi-antenna system demonstrate the efficiency of PPR-UCB in identifying effective equilibrium solutions within a few data samples.