Backscatter-Assisted High-Speed Rail Communications in Straight Tunnel Environments: Effects of Tag Number and Phase Control
Bibliographic record
Abstract
Backscatter communication is a promising technology to enhance the signal strength received by the receiver in straight tunnel environments. The impact of the number of tags and their phase adjustment on system performance remains a challenging issue though. Therefore, in this paper, we investigate the channel gain of backscatter-assisted communication with multiple tags in straight tunnels. In particular, we derive the probabilities that the backscatter link gain is greater than the direct link under adjustable and random phase assumptions by applying the Gaussian and Gamma approximations to derive tractable expressions. The simulation results show that phase-adjustable tags significantly improve the channel gain of the backscatter links compared to the random phase case. Moreover, the number of tags has an upper threshold for an effective tag deployment pattern. These insights provide valuable guidelines for the efficient design of backscatter communication systems in tunnel environments.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".