Deep Q-Networks Assisted Pre-connect Handover Management for 5G Networks
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".