An Adaptive Impedance-Matching System for Vehicular Power Line Communication
Bibliographic record
Abstract
Vehicular power line communication (VPLC) is being considered as a potential solution to mitigate the increase of complexity and cost of the automotive wiring harness caused by the growth of the number of electronic devices and sensors deployed inside vehicles. This is because VPLC reuses power cables for data communication and, thus, avoids the need for additional communication wires. However, this reuse does not come without problems. One of the challenges for VPLC is the time, frequency, and location dependence of the access impedance, which can cause severe impedance mismatch for the communication signal. Impedance mismatch degrades the signal-to-noise-power ratio at the communication receiver and thus affects transmission reliability. Due to the variable nature of the access impedance, a fixed matching circuit will be inefficient. Hence, in this work, we present an adaptive impedance-matching system, which improves the communication-signal transfer from the transmitting to the receiving device. The system is evaluated via simulations for a wide range of access-impedance test points and $S$- parameters of VPLC networks obtained in previous measurement campaigns. Our simulation results demonstrate that the presented adaptive matching system is able to achieve VPLC signal-power transfer within 30% of the theoretical optimum. This translates to a power gain, up to a factor of 10, which is larger than that of other solutions reported in the literature.
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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 | 0.000 | 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 | 0.001 | 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 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".