Deep Adaptive Transmission for Internet of Vehicles (IoV)
Why this work is in the frame
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Bibliographic record
Abstract
To support reliable transmission of data at high rate in time-varying fading channels, adaptive transmission is required, where transmitter and receiver adjust their transmission and reception mode to the dynamics of the channel. The receiver, based on its channel estimation and prediction, decides the optimal link adaptation and feeds this back to the transmitter. In this paper, we develop a deep learning (DL)-based link adaptation algorithm for highly dynamic communication links, where adaptive transmission parameters are decided for l > 1 forward time steps without a priori knowledge on channel statistics. Compared to conventional solutions, our approach reduces the feedback requirements from the receiver to the transmitter by a factor of l which significantly reduces the complexity. This achievement comes at no additional cost on the data rate and/or bit error rate.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it