Finite state Markov modelling for high speed railway wireless communication channel
Bibliographic record
Abstract
How to provide reliable, cost-effective wireless services for high-speed railway (HSR) users attracts increasing attention due to the fast deployment of HSRs worldwide. A key issue is to develop reasonably accurate and mathematically tractable models for HSR wireless communication channels. Finite-state Markov chains (FSMCs) have been extensively investigated to describe wireless channels. However, different from traditional wireless communication channels, HSR communication channels have the unique features such as very high speed, deterministic mobility pattern and frequent handoff events, which are not described by the existing FSMC models. In this paper, based on the Winner II physical layer channel model parameters, we propose a novel FSMC channel model for HSR communication systems, considering the path loss, fast fading and shadowing with high mobility. Extensive simulation results are given, which validate the accuracy of the proposed FSMC channel model. The model is not only ready for performance analysis, protocol design and optimization for HSR communication systems, but also provides an effective tool for faster HSR communication network simulation.
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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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".