Channel Estimation and Simulation of an Indoor Power-Line Network via a Recursive Time-Domain Solution
Bibliographic record
Abstract
The frequency-selective characteristics of a power-line channel is a result of the tree-like structure of the power-line network and the various loads connected to its termination points. Channel modeling is important for the estimation and compensation of the degradation suffered by the signal propagating over power lines, and is also the basis for channel simulation. Based on the previously proposed time-domain models, the authors present a new method to efficiently calculate a power-line channel's frequency response. With the knowledge about the power-line network's topology and cable characteristics, this algorithm utilizes simple but effective recursive matrix operations to accurately estimate the channel response of a particular power-line network. This method can be used to estimate the frequency response of any point-to-point channel in a power-line network, and can also be exploited as a channel simulation tool which provides a remarkably realistic description of a power-line channel. The effectiveness of the proposed algorithm is demonstrated by comparing the estimated and measured responses of two example power-line networks.
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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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".