Due to the increasing deployment of heterogeneous networks (HetNets), the selection of which radio access technologies (RATs) for Internet of Things (IoT) devices such as user equipments (UEs) has recently received extensive attention in mobility management research. Most of existing RAT selection methods only optimize the selection strategies from the UE side or network side, which results in heavy network congestion, poor user experience and system utility degradation. In this paper the UE side and the network side are considered comprehensively, based on the game theory (GT) model we propose a reinforcement learning with assisted network information algorithm to overcome the crucial points. The assisted information is formulated as a semi-Markov decision process (SMDP) provided for UEs to make accurate decisions, and we adopt the iteration approach to reach the optimal policy. Moreover, we investigate the impacts of different parameters on the system utility and handover performance. Numerical results validate that our proposed algorithm can mitigate unnecessary handovers and improve system throughputs.

由于异构网络 (HetNets) 的部署不断增加，最近在移动管理研究中，为物联网 (IoT) 设备（例如用户设备 (UE)）选择哪些无线接入技术 (RAT) 受到了广泛关注。现有的RAT选择方法大多只从UE侧或网络侧优化选择策略，导致网络拥塞严重、用户体验差、系统效用下降。本文综合考虑了UE侧和网络侧，基于博弈论（GT）模型，提出了一种强化学习辅助网络信息算法来攻克关键点。辅助信息被制定为提供给 UE 以做出准确决策的半马尔可夫决策过程 (SMDP)，我们采用迭代方法来达到最优策略。此外，我们研究了不同参数对系统效用和切换性能的影响。数值结果验证了我们提出的算法可以减少不必要的切换并提高系统吞吐量。