An evaluation of frequency domain PLC interference cancellation for DSL systems
Bibliographic record
Abstract
Recent advances in power line communications (PLC) have made it popular for in-home networking. This makes PLC an increasingly relevant source of interference for digital subscriber line (DSL) networks within the home environment. This paper presents measured PLC-to-DSL coupling channels for a worst case DSL deployment scenario. We then propose an interference cancelling scheme based on a frequency domain interference canceller (FDIC) that utilizes the common mode (CM) PLC interference to estimate and remove the differential mode (DM) PLC interference. The mean square error (MSE) of the proposed FDIC is derived and compared to the minimum MSE achieved by an optimum Wiener filter. Improvement in the signal to interference plus noise ratio (SINR) achieved by using the FDIC is also presented. Both mathematical analysis and simulation results demonstrate the effectiveness of the proposed FCID in reducing the DM PLC interference on the DM DSL signal.
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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 | 0.000 | 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".