Configuration of Network Level Algorithms for Wireless Train Control Systems using Physics-Based Propagation Models
Bibliographic record
Abstract
In communication-based train control (CBTC) systems, as a train travels along a track, it changes its association from one access point (AP) to another, which is referred to as a handoff. Control parameters for handoff algorithms are often set based on desired system requirements, such as outage probability, without considering the effect of the underlying propagation characteristics of the channel on the system performance. Therefore, for typical CBTC systems, conservative safety margins are introduced in order to ensure safe operations leading to potentially sub-optimal system performance. In this paper, we propose a systematic procedure for selecting a control parameter, namely the threshold value, which determines when handoffs are triggered for received signal strength (RSS) based handoff algorithms. The proposed scheme leads to the selection of threshold values that increase the safety margin in addition to reducing the number of handoffs incurred, thereby enhancing the overall CBTC system performance. These enhancements are made possible with the integration of physics-based propagation models and network design that harnesses the propagation characteristics of the underlying wireless channels.
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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".