Multi-Agent Deep Reinforcement Learning Assisted Pre-connect Handover Management
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".