用贝叶斯优化求解基站间资源分配的稳定均衡，仅需少量采样即可收敛。
Bayesian Optimization for Non-Cooperative Game-Based Radio Resource Management
- 基于贝叶斯优化构建可学习的非合作博弈框架，逐步逼近纯纳什均衡。
- 在多小区多天线系统中，仅用少量样本就找到有效功率分配方案。
- 适用于黑箱评估成本高、各基站目标冲突的无线资源管理场景。
现代蜂窝网络中的无线资源管理常需优化复杂且可能相互冲突的效用函数，尤其当各基站（BS）的效用只能通过昂贵的黑箱评估获取时，协调资源分配以确保网络服务稳定尤为困难。本文将频谱共享基站间的资源分配建模为非合作博弈，目标是使各基站的分配激励趋于稳定结果。为此，提出新型贝叶斯优化策略PPR-UCB，通过序列决策-评估对学习，近似纯纳什均衡（PNE）。PPR-UCB利用鞅技术对高斯过程（GP）代理模型构建高概率置信区间，实现效用不确定性的量化。在多小区多天线系统的下行链路发射功率分配实验中，验证了该方法在少数数据样本内高效识别有效均衡解的能力。
原文摘要 · Abstract (English)
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.
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