论文标题
无线电资源管理的贝叶斯优化：开路电源控制
Bayesian Optimization for Radio Resource Management: Open Loop Power Control
论文作者
论文摘要
我们为读者提供了使用高斯流程（BOGP）对贝叶斯优化的访问但严格的介绍，目的是解决各种无线电资源管理（RRM）问题。我们认为，BOGP是一种强大的工具，在RRM研究中被忽略了，尽管它优雅地满足了对快速收敛，安全探索和可解释性的紧迫要求。 BOGP还提供了一种自然的方法来利用优化期间的先验知识。在解释了BOGP的螺母和螺栓之后，我们深入研究了更高级的主题，例如，采集函数的选择以及动态性能函数的优化。最后，我们在5G蜂窝网络中的上行链路开放环功率控制（OLPC）的RRM问题付诸实践，为此，BOGP能够在探索过程中没有明显的性能下降的数十个迭代中收敛到几乎最佳的解决方案。
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.
