Design of a Short Reference Noncoherent Chaos-Based Communication Systems
Bibliographic record
Abstract
Data rate and energy efficiency decrement caused by the transmission of reference and data carrier signals in equal portions constitute the major drawback of differential chaos shift keying (DCSK) systems. To overcome this dominant drawback, a short reference DCSK system (SR-DCSK) is proposed. In SRDCSK, the number of chaotic samples that constitute the reference signal is shortened to R such that it occupies less than half of the bit duration. To build the transmitted data signal, P concatenated replicas of R are used to spread the data. This operation increases data rate and enhances energy efficiency without imposing extra complexity onto the system structure. The receiver uses its knowledge of the integers R and P to recover the data. The proposed system is analytically studied and the enhanced data rate and bit energy saving percentages are computed. Furthermore, theoretical performance for AWGN and multipath fading channels are derived and validated via simulation. In addition, optimising the length of the reference signal R is exposed to detailed discussion and analysis. Finally, the application of the proposed short reference technique to the majority of transmit reference systems such as DCSK, multicarrier DCSK, and quadratic chaos shift keying enhances the overall performance of this class of chaotic modulations and is, therefore, promising.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".