论文标题
通过可扩展的多目标贝叶斯优化的无细胞数据功率控制
Cell-Free Data Power Control Via Scalable Multi-Objective Bayesian Optimisation
论文作者
论文摘要
无单元的多用户多用户多输入多个输出网络是经典蜂窝体系结构的有前途替代品，因为它们有可能在网络的整个覆盖范围内提供统一的服务质量和高资源利用。为了实现这一潜力，以前的工作已经使用各种优化引擎开发了无线电资源管理机制。在这项工作中，我们考虑了在无细胞网络中上行链路链路数据功率控制的背景下，总体上沿频谱效率最大化的问题。为了在大型网络中解决此问题并解决收敛时间限制，我们采用可扩展的多目标贝叶斯优化。此外，我们讨论了多保真仿真和贝叶斯优化的交集如何改善无细胞网络中的无线电资源管理。
Cell-free multi-user multiple input multiple output networks are a promising alternative to classical cellular architectures, since they have the potential to provide uniform service quality and high resource utilisation over the entire coverage area of the network. To realise this potential, previous works have developed radio resource management mechanisms using various optimisation engines. In this work, we consider the problem of overall ergodic spectral efficiency maximisation in the context of uplink-downlink data power control in cell-free networks. To solve this problem in large networks, and to address convergence-time limitations, we apply scalable multi-objective Bayesian optimisation. Furthermore, we discuss how an intersection of multi-fidelity emulation and Bayesian optimisation can improve radio resource management in cell-free networks.
