Cramér-Rao Lower Bound for Power Line Integrated Communication and Sensing
Bibliographic record
Abstract
Power line communication (PLC) modems are transmitting high-frequency signals through the electric grid infrastructure. These signals can also be interpreted as probing or sensing signals. PLC is thus a natural candidate for integrated sensing and communication (ISAC). The implementation of grid sensing or monitoring with PLC has already been studied in previous work. In this paper, we ask the question what accuracy for (fault) parameter estimation can be achieved through power line ISAC. Since this is the first study of this kind, we are mainly concerned with introducing methodology. We adopt the Cramér-Rao lower bound (CRLB) as a performance criterion that is universally applicable regardless of the specific ISAC implementation. We show how to connect measurement variables and unknown parameters typically experienced in grid monitoring to obtain expressions for the CRLB. Using transmission-line modeling for PLC signal propagation, we identify automatic differentiation as a suitable tool to evaluate those expressions. The effectiveness of power line ISAC is illustrated through numerical results for a fault location estimation use case.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".