Opportunistic spectral access through suppression of impulsive interference
Bibliographic record
Abstract
Cognitive radios are slated to be the next generation of smart transceivers that can opportunistically access spectrum through dynamic sensing of their immediate radio frequency (RF) environment. Such spectral sharing will be limited primarily by the interference that a cognitive user may potentially cause to the licensed primary user of the band. In particular, all cognitive transmitters located within a certain region of interference of a primary user will have to refrain from transmitting data. Given that most cognitive users will need to transmit data only intermittently and that there will be only a finite number of such users in the RF neighbourhood of a primary user, it is conceivable that the resulting interference at the primary will be more structured than can be described by a white Gaussian noise model. This opens the door for interference mitigation techniques that exploit the interference structure. In this work, methods to mitigate the effects of potentially harmful interference caused by active secondary users through intelligent signal processing at the receiver of the primary user are investigated, such that the perimeter of the region of interference can be reduced, creating greater opportunities for the secondary users while meeting interference constraints. Receiver structures for the more practical scenario of temporally correlated interference are introduced, and the achievable gains when applying simple yet effective interference suppression methods are quantified.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".