A cognitive control approach to interference mitigation in communications-based train control (CBTC) co-existing with passenger information systems (PISs)
Bibliographic record
Abstract
As a key component of urban rail transit systems, communications-based train control (CBTC) is an automated train control system using train-ground communications to ensure efficient operation of rail vehicles. In addition to CBTC systems, passenger information systems (PISs) are adopted in urban rail transit systems to improve quality of service (QoS) offered to customers. The interference between CBTC systems and PISs is an important factor impacting QoS of both CBTC systems and PISs. With recent advances in cognitive dynamic systems, in this paper, we take a cognitive control approach to interference mitigation considering the co-existence of CBTC systems and PISs. In our cognitive control approach, the notion of information gap is adopted to quantitatively describe effects of interference on CBTC. The wireless channel is modeled as a finite-state Markov chain with multiple state transition probability matrices, which are derived from real field measurements. Simulation results show that the proposed cognitive control approach can significantly improve performance of CBTC train-ground communications under interference from co-existing PISs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".