Proactive Handover Type Prediction and Parameter Optimization Based on Machine Learning
Bibliographic record
Abstract
With the explosive growth of smart devices and applications, the demand for mobile service with higher data rate and better quality of service is growing rapidly. Ultra-dense networks, capable of providing higher network throughput, remain one of the key technologies for next-generation mobile communications. However, the densification of network further reduces the coverage of base stations and the distance between each other, which in turn leads to unnecessary and frequent handovers (HOs), affects the stability and reliability of communication links. HO failures can even occur due to the improper HO control parameter (HCP) values. To this end, a HO type prediction and parameter optimization method based on machine learning is proposed. First, the HO is divided into four categories: successful handover (SHO), ping-pong handover (PPHO), too-late handover (TLHO), and too-early HO (TEHO). Second, we combine reinforcement learning with supervised learning and propose a novel adaptive HCP adjusting scheme. Specifically, deep Q-network dynamically selects HCP values through environmental information and supervised learning-based HO prediction results. Simulation results demonstrate that our proposed scheme achieves a prediction accuracy of 94.83%, while reducing the PPHO rate by 15%, the TEHO rate by 2%, and the TLHO rate by 3%.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".