基于双深度强化学习的切换算法分析

Handover algorithm analysis based on dual deep reinforcement learning

由于包括毫米波频率,导致5G网络中的切换更具挑战性,基站(BS)部署更加密集。由于毫米波BS的占用空间较小,进一步增加了切换的数量,从而使切换管理成为一项更关键的任务。因为随着切换数量的增加,降低了服务质量(QoS)和体验质量(QoE),以及更高的信令开销。文章讨论了一种基于双深度强化学习(DDRL)的离线方案,以最小化毫米波网络中切换的频率,从而减轻不利的QoS。由于考虑到的5G环境的固有特性,会产生连续且大量的状态空间,因此与传统的Q学习算法相比,DDRL更可取。

Handovers(HO)have been envisioned to be more challenging in 5G networks due to the inclusion of millimeter wave(mmwave)frequencies,resulting in more intense base station(BS)deployments.This,by its turn,increases the number of HO taken due to smaller footprints of mm-wave BS thereby making HO management a more crucial task as reduced quality of service(QoS)and quality of experience(QoE)along with higher signalling overhead are more likely with the growing number of HO.In this paper,we propose an offline scheme based on double deep reinforcement learning(DDRL)to minimize the frequency of HOs in mm-wave networks,which subsequently mitigates the adverse QoS.Due to continuous and substantial state spaces arising from the inherent characteristics of the considered 5G environment,DDRL is preferred over conventional Q-learning algorithm.