Robust spectrum sensing for orthogonal frequency division multiplexing signal without synchronization and prior noise knowledge
Bibliographic record
Abstract
Spectrum sensing is defined as the task of detecting the presence of licensed users and is an essential prerequisite for opportunistic spectrum access in cognitive radio. Motivated by the infeasible assumptions of perfect synchronization and prior noise knowledge in most of the existing spectrum sensing algorithms, a robust orthogonal frequency division multiplexing OFDM signal sensing scheme, with the use of a noise power insensitive threshold, is investigated in this paper. Identification of primary OFDM signals is achieved by sliding the local pilot reference over the received signals and measuring the frequency domain correlations. The necessity of prior noise power knowledge for the sensing threshold determination is removed by employing the proposed interference insensitive test metric, which is a ratio of uniformly distorted correlations. As a result, no noise power information is required in the sensing process. In addition, the effects of both timing and frequency offsets are mitigated with a novel extended time domain segmentation as well as multiple frequency domain correlations via a frequency sliding window. Numerical results are provided to validate the theoretical analysis and estimate the performance of the proposed algorithm. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".