Spectrum Monitoring Using Energy Ratio Algorithm For OFDM-Based\n Cognitive Radio Networks
Bibliographic record
Abstract
This paper presents a spectrum monitoring algorithm for Orthogonal Frequency\nDivision Multiplexing (OFDM) based cognitive radios by which the primary user\nreappearance can be detected during the secondary user transmission. The\nproposed technique reduces the frequency with which spectrum sensing must be\nperformed and greatly decreases the elapsed time between the start of a primary\ntransmission and its detection by the secondary network. This is done by\nsensing the change in signal strength over a number of reserved OFDM\nsub-carriers so that the reappearance of the primary user is quickly detected.\nMoreover, the OFDM impairments such as power leakage, Narrow Band Interference\n(NBI), and Inter-Carrier Interference (ICI) are investigated and their impact\non the proposed technique is studied. Both analysis and simulation show that\nthe \\emph{energy ratio} algorithm can effectively and accurately detect the\nappearance of the primary user. Furthermore, our method achieves high immunity\nto frequency-selective fading channels for both single and multiple receive\nantenna systems, with a complexity that is approximately twice that of a\nconventional energy detector.\n
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".