Second-order cyclostationarity-based detection and \nclassification of LTE SC-FDMA signals for \ncognitive radio
Bibliographic record
Abstract
Cognitive radio (CR) was developed for utilizing the spectrum bands efficiently. Spectrum \nsensing and awareness represent main tasks of a CR, providing the possibility \nof exploiting the unused bands. \nIn this thesis, we investigate the detection and classification of Long Term Evolution \n(LTE) single carrier-frequency division multiple access (SC-FDMA) signals, which are \nused in uplink LTE, with applications to cognitive radio. We explore the second-order \ncyclostationarity of the LTE SC-FDMA signals, and apply results obtained for the \ncyclic autocorrelation function to signal detection and classification (in other words, \nto spectrum sensing and awareness). The proposed detection and classification algorithms \nprovide a very good performance under various channel conditions, with a \nshort observation time and at low signal-to-noise ratios, with reduced complexity. The \nvalidity of the proposed algorithms is verified using signals generated and acquired \nby laboratory instrumentation, and the experimental results show a good match with \ncomputer simulation results.
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 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".