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.
翻译:我们向读者介绍巴伊西亚最优化高斯进程(BOGP),目的是解决各种无线电资源管理问题。我们认为,BOGP是一个强有力的工具,在RRM研究中有些被忽略,尽管它优雅地解决了快速趋同、安全探索和可解释的迫切要求。BOGP还提供了在优化过程中利用先前知识的自然方法。在解释BOGP的坚果和螺栓之后,我们深入探讨更先进的课题,例如采购功能的选择和动态性能功能的优化。最后,我们将RRM的理论应用于5G蜂窝网络的上链路电源控制(OLPC)问题,BOGP能够在勘探期间不发生重大性能下降的情况下,在数十次循环中将这种理论汇集到几乎最佳的解决办法中。