Doppler frequency estimation‐based handover algorithm for long‐term evolution networks
Why this work is in the frame
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Bibliographic record
Abstract
Mobile cellular radio systems have become complex multi‐layered systems with a mixed architecture of macro and micro cells; therefore, a dynamic method of triggering the handover algorithm in such systems is invaluable and required. In this paper the authors develop a simple handover mechanism to adapt for a fast moving mobile station requesting a handover, in which the authors utilise a Doppler frequency estimation in the downlink for adjustment and apply it to a long‐term evolution (LTE) network. This method is beneficial for high speed mobiles in macro and/or micro cells, in which for the latter the cell radius is small and needs a dynamic algorithm to respond in a timely manner. The main objective of this study is to investigate the performance of the proposed algorithm; hence, a system layout is specifically designed for this study. Other factors, such as interference, which may affect the performance of the suggested system are not addressed here. The main concern is to study and compare the proposed algorithm to the standard handover algorithm currently implemented in LTE. Simulations at the system level show a marked decrease in the average number of handovers requested.
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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 | 0.001 |
| 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 it