Integrated Sensing and Communications for End-to-End Predictive Beamforming Design in Vehicle-to-Infrastructure Networks
Bibliographic record
Abstract
Integrated sensing and communications (ISAC) has emerged as a promising technology for predictive beamforming in vehicle-to-infrastructure (V2I) networks. Most of the existing works on ISAC assume each vehicle is equipped with a single antenna and use a two-phase scheme for predictive beamforming design. In the first phase, the reflected sensing signals at the roadside unit (RSU) are used to estimate the state parameters (e.g., angle, channel state information (CSI)) of the vehicles. In the second phase, the beamformer is predicted based on the estimated state parameters. The two-phase scheme suffers from the drawback that the estimation error in the first phase can impact the beamformer design in the second phase and may lead to a degradation in the achievable rate. In this work, we design predictive beamformers for both the RSU and vehicles in an end-to-end manner by using deep learning. We propose one-sided predictive beamforming (OSPB) and two-sided predictive beamforming (TSPB) schemes, where the beamformers for the vehicles are determined by the RSU and by the vehicles themselves, respectively. Both schemes directly predict the beamformers based on the reflected sensing signals via deep neural networks (DNNs). Compared with the existing two-phase schemes, the proposed schemes bypass the intermediate parameter estimation phase, thereby mitigating the impact of parameter estimation error. Our simulation results demonstrate the advantages of the proposed schemes over the two-phase baseline schemes in terms of achievable sum-rate.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".