Full-duplex spectrum sensing in broadband power line communications
Bibliographic record
Abstract
We address the issue of electromagnetic interference between power line communication (PLC) and non-PLC services by using full-duplex spectrum sensing for cognitive PLC. Contemporary broadband PLC (BB-PLC) standards allow PLC devices to dynamically use idle spectra that are allotted to non-PLC services, like broadcast radio. Traditional spectrum sensing techniques operate in a half-duplex manner, and thus provide sub-optimal sensing efficiency. In this paper, we propose the use of in-band full-duplex (IBFD) operation to allow simultaneous data transmission and spectrum sensing, and eliminate all sensing-only and other wait times involved in conventional spectrum sensing operations. We first show that using the state-of-the-art IBFD solution yields inaccurate spectrum sensing performance due to non-negligible residual self-interference. We therefore propose multiple solutions to address this problem. We show through simulation results that our proposed solutions provide sufficient self-interference cancellation to achieve nearly the same spectrum sensing accuracy as that obtained in a half-duplex operation.
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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 | 0.001 | 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".