Improved performance at higher data rates of DSRC systems using a linear demapper
Bibliographic record
Abstract
This paper proposes and designs a linear demapper which compares to a recently proposed non-linear demapper under different decoding schemes of dedicated short-range communications (DSRC) receivers. Modelling and simulations are carried out under common mobile vehicular channel conditions. It can easily be seen that multipath and Doppler shift has a detrimental effect on the transmitted modulated messages. The effect of hard and soft demapper choice on BER performance of the DSRC Physical Layer (PHY) is shown for comparison. Digital and analog circuit elements are modeled and imperfections are taken into account in the simulations. Compared to the conventional system, our proposed system improves lower code rate demapping accuracy and substantially improves high code rate transmissions. Simulation results confirm that our proposed scheme can offer performance advantage of around a couple of orders of magnitude over both the conventional and non-linear demapper designs at higher data rates.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 |
| Open science | 0.004 | 0.004 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".