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

页数：6

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