Deep Q-Networks Assisted Pre-connect Handover Management for 5G Networks

被引:1
作者
Wei, Yao [1 ]
Lung, Chung-Horng [1 ]
Ajila, Samuel [1 ]
Cabrera, Ricardo Paredes [2 ]
机构
[1] Carleton Univ, Dept Syst & Comp Engn, Ottawa, ON, Canada
[2] Ericsson Canada Inc, Ottawa, ON, Canada
来源
2023 IEEE 97TH VEHICULAR TECHNOLOGY CONFERENCE, VTC2023-SPRING | 2023年
关键词
Handover Management; 5G Networks; Deep Reinforcement Learning; Deep Q-Networks;
D O I
10.1109/VTC2023-Spring57618.2023.10199527
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Handover management is crucial for wireless networks and is more challenging for Fifth Generation (5G) networks due to strict requirements in quality of service (QoS), such as ultra-reliable low latency communications (URLLC) services. This paper extends the pre-connect handover (PHO) mechanism for user equipment (UE) using Deep Q-Networks (DQN) to support challenging handover management requirements in 5G networks. The proposed DQN-assisted PHO management facilitates the sequential decision-making problem of the target cell selection based on the Reference Signal Received Quality (RSRQ) values and RSRQ change rates of all the candidate cells. The performance of the DQN-assisted PHO management solutions has been evaluated extensively with various configurations using Network Simulator 3 (NS-3) and NS3-Gym. The experimental results demonstrated the DQN-assisted PHO technique can productively accomplish the optimal target cell selection to maximize the success rate of PHO.
引用
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页数:6
相关论文
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