Two-Stage Beamforming Design for High-Speed Train mmWave Communications
Bibliographic record
Abstract
Millimeter wave (mmWave) communications can achieve high data-rate transmission for high-speed trains (HSTs). However, the rapid change in path loss during the fast movement of HSTs poses a significant challenge to the mm Wave beamforming design. In this paper, a two-stage beam-forming (TSB) scheme is proposed to address this challenge for downlink HST mmWave communications. In the first stage, an algorithm based on semi-definite relaxation (SDR) and alternating minimization (AM) is proposed to stabilize the instantaneous receive signal-to-noise ratio (SNR) above a predefined threshold when the HSTs travel along the railway. In the second stage, the coverage of each beam used by the base station (BS) is widened to reduce the number of beam switches. Simulation results demonstrate that the proposed scheme requires fewer BS beams to cover the same railway range than the existing schemes while keeping the instantaneous receive SNR of the HSTs above the predefined threshold.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".