Multi-Agent Deep Reinforcement Learning Assisted Pre-connect Handover Management

It appears your Web browser is not configured to display PDF files. Download adobe Acrobat or click here to download the PDF file.

Click here to download the PDF file.


Wei, Yao




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.


Artificial Intelligence
Computer Science




Carleton University

Thesis Degree Name: 

Master of Applied Science: 

Thesis Degree Level: 


Thesis Degree Discipline: 

Engineering, Electrical and Computer

Parent Collection: 

Theses and Dissertations

Items in CURVE are protected by copyright, with all rights reserved, unless otherwise indicated. They are made available with permission from the author(s).