Efficient Handover Mechanism for Radio Access Network Slicing by Exploiting Distributed Learning

Abstract

Network slicing is identified as a fundamental architectural technology for future mobile networks since it can logically separate networks into multiple slices and provide tailored quality of service (QoS). However, the introduction of network slicing into radio access networks (RAN) can greatly increase user handover complexity in cellular networks. Specifically, both physical resource constraints on base stations (BSs) and logical connection constraints on network slices (NSs) should be considered when making a handover decision. Moreover, various service types call for an intelligent handover scheme to guarantee the diversified QoS requirements. As such, in this article, a multi-agent reinforcement LEarning based Smart handover Scheme, named LESS, is proposed, with the purpose of minimizing handover cost while maintaining user QoS. Due to the large action space introduced by multiple users and the data sparsity caused by user mobility, conventional reinforcement learning algorithms cannot be applied directly. To solve these difficulties, LESS exploits the unique characteristics of slicing in designing two algorithms: 1) LESS-DL, a distributed ${Q}$ -learning algorithm to make handover decisions with reduced action space but without compromising handover performance; 2) LESS-QVU, a modified ${Q}$ -value update algorithm which exploits slice traffic similarity to improve the accuracy of ${Q}$ -value evaluation with limited data. Thus, LESS uses LESS-DL to choose the target BS and NS when a handover occurs, while ${Q}$ -values are updated by using LESS-QVU. The convergence of LESS is theoretically proved in this article. Simulation results show that LESS can significantly improve network performance. In more detail, the number of handovers, handover cost and outage probability are reduced by around 50%, 65%, and 45%, respectively, when compared with traditional methods.