Beam Alignment in mmWave V2X Communications: A Survey
Bibliographic record
Abstract
The digital transformation within the automotive industry is accelerating towards an era dominated by autonomous vehicles, with vehicle-to-everything (V2X) communications being a fundamental enabler for this advancement. As vehicular networks evolve to meet the complex demands of autonomous driving, traditional communication systems encounter limitations in bandwidth and data transfer rates. Millimeter-wave (mmWave) communication emerges as a pivotal solution, offering the extensive bandwidth required for the high data throughput and low latency essential in modern vehicular communications. However, challenges loom, with beam alignment in mmWave V2X becoming a time-consuming process and the mmWave’s blockage effect impeding consistent and reliable vehicular communication links. Therefore, the development of efficient, real-time, and robust beam alignment technology is crucial for mmWave V2X communication. In this paper, we present a comprehensive survey of beam alignment techniques in mmWave V2X communication. We explore various approaches including beam sweeping, angle of arrival (AoA)/angle of direction (AoD) estimation, black-box optimization, and side information. Subsequently, we introduce performance metrics for assessing beam alignment performance and compare the performance of four beam alignment methods under different metrics. Finally, we summarize the future research directions and challenges faced by beam alignment techniques in mmWave V2X communication, offering valuable insights for researchers in this field.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".