Towards mobility-aware predictive radio access
Bibliographic record
Abstract
Novel radio access techniques that leverage mobility predictions are receiving increasing interest in recent literature. The essence of these schemes is to lookahead at the future rates users will experience, and then devise long-term resource allocation strategies. For instance, a YouTube video user moving towards the cell edge can be prioritized to pre-buffer additional video content before poor coverage commences. While the potential of mobility-aware resource allocation has recently been demonstrated, several practical design aspects and evaluation approaches have not yet been addressed due to the complexity of the problem. Furthermore, since prior works have focused on specific applications there is also a strong need for a unified framework that can support different user and network requirements. For this purpose, we present a novel two-stage Predictive Radio Access Network (P-RAN) framework that can efficiently leverage both future data rate predictions in the order of tens of seconds, and instantaneous fast fading at the millisecond level. We also show how the framework can be implemented within the open source Network Simulator 3 (ns-3) LTE module, and apply it to optimize stored video delivery. A thorough set of performance tests are then conducted to assess the performance gains and investigate sensitivity to various prediction errors. Our results indicate that P-RANs can jointly improve both service quality and transmission efficiency. Additionally, we also observe that P-RAN performance can be further improved by modeling prediction uncertainty and developing robust allocation techniques.
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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".