An enhanced data rate chaos-based multilevel transceiver design exploiting ergodicity
Bibliographic record
Abstract
Conventionally, practical chaos-based communication transceivers have failed to offer supportable data rates of the order of Mbps. The actual feasibility of such a high data rate chaos-based transceiver has been successfully addressed by exploiting the basics of Ergodic Theory. The proposed implementation is based on the Ergodic Chaotic Parameter Modulation (ECPM) scheme. However, the designed transceiver could only support a data rate of about 1–2 Mbps. Therefore, in order to address real-time applications with data rate requirements of the order of tens of Mbps, the supportable data rate of the practical transceiver needed enhancement. The issue of further increasing the data rate has been addressed by using multilevel Quadrature Amplitude Modulation (QAM) together with the basics of ECPM. Although, fundamentals of theoretical evaluation proposed the possibility of a M-level QAM-ECPM transceiver, the actual feasibility of such a transceiver still remained ambiguous. A primary factor contributing to this lack of feasibility is the need for practically viable solutions of suitable chaotic maps that can support multilevel schemes. This paper presents the design and implementation of a practical multilevel ECPM transceiver with data rate of the order of 48 Mbps. Theoretical as well as practically achievable data rates have also been estimated. Design of suitable chaotic maps required to make the proposed transceiver a reality has also been addressed. Power efficiency and resource usage of the designed prototype has been evaluated based on both Altera Stratix FPGA implementation as well as silicon.
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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 | 0.000 |
| 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) | 0.002 | 0.001 |
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".