Latency-Reduced Equalizer with Model-Based Channel Estimation for Vehicle-to-Vehicle Communications
Bibliographic record
Abstract
Equalization of fast time-varying channels is impacted by short coherence time and deep signal fading, especially in vehicle-to-vehicle communications due to the high mobility of user terminals. Furthermore, the stringent latency requirement of safety applications, such as collision avoidance, cannot tolerate high-complexity operations and long processing time. In order to achieve both requirements of equalization accuracy and short latency for fast varying channels, we propose a new model-based time-domain equalizer. In this equalizer, estimation and equalization are performed in two parallel parts to shorten the processing time. In main path, data symbols pass through an equalizer preset with the up-to-date channel impulse response. Since the channel variation model remains invariable for sufficiently long time, the current channel is estimated in parallel path from a number of past channel impulse responses to improve the accuracy. However, the channel variation during the long processing time of estimation leads to equalization error. Therefore, a predictor is used to update the channel response related to the processing delay, and perform the channel estimation beyond the channel coherence time. Thus, high accuracy and delay-free equalization can be achieved through this parallel structure.
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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 | 0.001 |
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".