We provide the reader with an accessible yet rigorous introduction to Bayesian optimisation with Gaussian processes (BOGP) for the purpose of solving a wide variety of radio resource management (RRM) problems. We believe that BOGP is a powerful tool that has been somewhat overlooked in RRM research, although it elegantly addresses pressing requirements for fast convergence, safe exploration, and interpretability. BOGP also provides a natural way to exploit prior knowledge during optimization. After explaining the nuts and bolts of BOGP, we delve into more advanced topics, such as the choice of the acquisition function and the optimization of dynamic performance functions. Finally, we put the theory into practice for the RRM problem of uplink open-loop power control (OLPC) in 5G cellular networks, for which BOGP is able to converge to almost optimal solutions in tens of iterations without significant performance drops during exploration.

中文翻译：

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无线电资源管理的贝叶斯优化：开环功率控制


我们为读者提供了关于高斯过程贝叶斯优化 (BOGP) 的简单易懂且严格的介绍，旨在解决各种无线电资源管理 (RRM) 问题。我们相信 BOGP 是一个强大的工具，尽管它优雅地满足了快速收敛、安全探索和可解释性的紧迫要求，但在 RRM 研究中却被忽视了。 BOGP 还提供了一种在优化期间利用先验知识的自然方法。在解释了 BOGP 的具体细节之后，我们深入研究了更高级的主题，例如采集函数的选择和动态性能函数的优化。最后，我们将理论应用于 5G 蜂窝网络中上行链路开环功率控制（OLPC）的 RRM 问题的实践，为此，BOGP 能够在数十次迭代中收敛到几乎最优的解决方案，而在探索过程中不会出现明显的性能下降。