An Intelligent Detection Based on Deep Learning for Multilevel Code Shifted Differential Chaos Shift Keying System With <i>M</i>-ary Modulation
Bibliographic record
Abstract
Multilevel code shifted${M}$-ary differential chaos shift keying (MCS-MDCSK) system provides higher data rate chaotic information transmission by applying multilevel code shifting aided${M}$-ary modulation. However, the real-valued chaotic sequences induce interferences to signals while higher-order modulation shortens the Euclidean distance between adjacent symbols, thereby leading to performance degradation. To improve the bit-error rate (BER) performances, we propose an intelligent detector to achieve the joint demodulation and de-spreading at the receiver. In this design, we construct the recursive long short-term memory (LSTM) unit to extract features from the correlated chaotic modulated signals. Then we concatenate the LSTM unit with multiple full connection layers (FCLs) and compose the deep neural network (DNN) to recover the information. Owing to the serial concatenated LSTM-aided DNN, the intelligent detector can learn the joint chaotic modulation and spreading pattern, and achieve the joint demodulation and de-spreading. Consequently, larger performance gain can be attained and the reliability performances will be improved. Simulation results validate the proposed design. Moreover, for practical systems undergoing multiplicative fading, the intelligent MCS-MDCSK detector exhibits better BER performances than the benchmark systems.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".