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- W4311681003 abstract "This thesis proposes a MBB adopted handover mechanism, namely, pre-connect handover (PHO). PHO aims to provide a seamless and reliable handover for 5G networks. PHO utilizes DQN algorithm to facilitate the sequential decision-making problem of target base station (BS) selection based on the RSRQs and RSRQ change rates of all the surrounding candidate BSs. A MADRL solution is tailored to extend the DQN-assisted UE-associated PHO management for modeling a multi-UE scenario, where the autonomous agents learn the action policy by interacting with the environment in a distributed manner. The feasibility of PHO has been validated extensively via NS-3 and NS3-Gym. The experimental results demonstrated that the proposed PHO is not only achievable, but also that the DQN-assisted PHO technique can productively accomplish the optimal BS selection to maximize the PHO success rate. Moreover, the MADRL-assisted solution can also be conducted and effectively applied to a realistic multi-UE environment." @default.
- W4311681003 created "2022-12-28" @default.
- W4311681003 creator A5021813420 @default.
- W4311681003 date "2022-12-16" @default.
- W4311681003 modified "2023-09-23" @default.
- W4311681003 title "Multi-Agent Deep Reinforcement Learning Assisted Pre-connect Handover Management" @default.
- W4311681003 doi "https://doi.org/10.22215/etd/2022-15213" @default.
- W4311681003 hasPublicationYear "2022" @default.
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