RAT selection for IoT devices in HetNets: Reinforcement learning with hybrid SMDP algorithm

Bian Hongyi; Zhang Qingmiao; Zhao JunhuiZhang Huan

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RAT selection for IoT devices in HetNets: Reinforcement learning with hybrid SMDP algorithm

机译：RAT selection for IoT devices in HetNets: Reinforcement learning with hybrid SMDP algorithm

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摘要

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. (c) 2022 Elsevier B.V. All rights reserved.

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《Physical Communication》 |2022年第10期|101833.1-101833.10|共10页
作者
Bian Hongyi; Zhang Qingmiao; Zhao JunhuiZhang Huan;
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作者单位

East China Jiaotong Univ;

East China Jiaotong Univ||Beijing Jiaotong Univ;

Beijing Jiaotong Univ;

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正文语种英语
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Heterogeneous networks (HetNets); Radio access technologies (RATs); Game theory (GT); Reinforcement learning; Semi-Markov decision process (SMDP);

RAT selection for IoT devices in HetNets: Reinforcement learning with hybrid SMDP algorithm

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