A Cascaded Approach to Efficient Detection of OFDM Signals Based on Energy and Autocorrelation Detection
Bibliographic record
Abstract
Spectrum sensing is one of the most important parts of the cognitive radio system. In order to detect the primary user signal, the robust sensing method is required. In this paper, we propose a combination of the energy detection method and the autocorrelation-based detection method with a cascaded architecture to improve the probability of detection. Energy detection involves lower complexity and energy consumption, but it is unreliable at low signal-to-noise ratios (SNRs). Orthogonal frequency division multiplexing signal property is used by an autocorrelation-based detector, which involves more complexity and is more robust at low SNRs. We build the cascaded design of both energy detection and autocorrelation-based detection to take advantage of each technique. The performance of a cascaded detector is then presented. It is shown that the probability of detection of the cascaded design is always better than the probability of detection of a single detector. The design of the cascaded detector has been implemented which does not require additional sensing time. The complexity and utilization of each detector design is then presented to show the efficiency of the proposed design.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".