Predictive Beamforming for OTFS-Enabled URLLC in High-Mobility Vehicular Networks
Bibliographic record
Abstract
Ultra reliable low latency communication (URLLC) in vehicular networks is pivotal for meeting the stringent requirements of transportation safety. However, achieving it is very challenging due to the high mobility of vehicles and the complex propagation environment. Orthogonal time frequency space (OTFS) modulation addresses these issues by modulating symbols into the delay-Doppler (DD) domain, which can leverage full timefrequency diversity by spreading each DD domain symbol across the entire time-frequency plane. In this paper, we propose a novel OTFS-enabled ultra reliable low latency vehicular network architecture for downlink transmission. To achieve low latency, we adopt frequency division duplex (FDD) mode to transmit data frames as soon as they arrive to minimize scheduling delays. Furthermore, to enhance the received signal strength at the receiver, beamforming is applied at the transmitter. Due to the channel state information (CSI) feedback delay in FDD systems, we design a deep learning algorithm, the DD-domain Convolutional Transformer (DDCT), for predictive beamforming based on historical DD-domain CSI. In DDCT, a convolutional neural network extracts spatial features from the DD domain, and a transformer captures their temporal correlations. Extensive simulation results demonstrate the effectiveness of the proposed vehicular network architecture and the superiority of the deep learning algorithm for predictive beamforming.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 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".