A Novel Recognition Method for Direct Sequence Spread Spectrum (DSSS) Signals Based on Secondary Power Spectrum
Bibliographic record
Abstract
DSSS signal is a technical means used for spread spectrum communication. Due to its unique frequency characteristics, it has great anti-interference and multipath resistance capabilities, and has many advantages such as a wide spectrum, making it an important enabling technology for spread spectrum communication. However, the relevant characteristics of spread spectrum signals pose challenges in detecting spread spectrum signals from conventional signals. Based on this, this article selects a feature value based on quadratic power spectrum to distinguish between conventional signals and spread spectrum signals after binary phase shift keying (BPSK) modulation, and selects decision boundaries through a large number of training sets, and tests the model. The results show that the spread spectrum signal detection method based on the secondary power spectrum has good detection accuracy and performance, with a discrimination accuracy of 99.44% and 99.6% for the training and testing sets, respectively, verifying the feasibility of this detection method.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".