Multiband joint detection with correlated spectral occupancy in wideband cognitive radios
Bibliographic record
Abstract
Recently, a wideband spectrum sensing scheme referred to as multiband joint detection has been proposed by Quan et al., in which a set of frequency dependent detection thresholds are optimized to achieve the best trade-off between aggregate measures of opportunistic throughput and interference to primary users in cognitive radio (CR) networks. While this scheme shows significant performance gains over benchmark approaches, it employs a frequency-decoupled detector structure that is not optimal in the presence of correlation between subband occupancies, a common situation in CR applications. In this paper, we investigate how a frequency-coupled optimum linear energy combiner (OLEC) structure, recently proposed for single user scenarios, can be integrated into the above multiband joint detection framework to take further advantage of subband occupancy correlation in wideband spectrum sensing. We first analyze the performance of the single-user OLEC and derive expressions for its probabilities of false alarm and missed detection. Using these expressions, we then formulate joint optimization problems for the detection thresholds used by a bank of subband OLECs, with the aim to maximize the aggregate opportunistic throughput under interference constraints. Through numerical experiments with a Markov model of subband occupancy, we show that the use of the OLEC in wideband spectrum sensing can significantly enhance CR performance in terms of these global metrics, when compared to the decoupled multiband processing structure.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".