论文标题
用于分布式节能资源分配的联合学习
Federated Learning for Distributed Energy-Efficient Resource Allocation
论文作者
论文摘要
在蜂窝网络中，资源分配是以集中式进行的，这为基站（BS）和高传输开销带来了巨大的计算复杂性。本文研究了蜂窝网络的分布式资源分配方案，以最大程度地提高系统在上行链路传输中的能源效率，同时保证蜂窝用户的服务质量（QOS）。特别是，为了应对无线通信环境中快速变化的渠道，我们建议一个强大的联合加固学习（FRL_SUC）框架，以使本地用户能够通过在每个用户培训的本地神经网络的指导下，在传输电源和渠道分配的项目中执行分布式资源分配。分析和数值结果表明，提出的FRL_SUC框架可以降低开销的传输，并从中央服务器到本地用户的计算，同时在EE方面超越了常规的多代理增强学习算法，并且更强大，并且更强大。
In cellular networks, resource allocation is performed in a centralized way, which brings huge computation complexity to the base station (BS) and high transmission overhead. This paper investigates the distributed resource allocation scheme for cellular networks to maximize the energy efficiency of the system in the uplink transmission, while guaranteeing the quality of service (QoS) for cellular users. Particularly, to cope the fast varying channels in wireless communication environment, we propose a robust federated reinforcement learning (FRL_suc) framework to enable local users to perform distributed resource allocation in items of transmit power and channel assignment by the guidance of the local neural network trained at each user. Analysis and numerical results show that the proposed FRL_suc framework can lower the transmission overhead and offload the computation from the central server to the local users, while outperforming the conventional multi-agent reinforcement learning algorithm in terms of EE, and is more robust to channel variations.
