Adaptive Parallel DCSK with Code Index Modulation for Implantable Sensor Networks
Bibliographic record
Abstract
Intra-body communication is limited by the complex biological organization of the human body, and the signal is subject to severe attenuation and multipath interference during propagation. In this paper, an adaptive parallel transmission with code-indexed modulation and differential chaotic shift keying (A-PT-CIM-DCSK) technique is proposed for implantable sensor networks. The number of parallel transmission channels is adaptively adjusted through least squares for channel estimation to achieve optimal transmission, enhancing the robustness of the system while maintaining a high data rate. The computational complexity is reduced by the cyclic shift of chaotic sequences. The theoretical bit error rate (BER) formula of A-PT-CIM-DCSK over the generalized Nakagami channel is derived, and its performance is verified by Monte Carlo simulations. The simulation results show that compared with the conventional CIM-DCSK system, the proposed scheme can achieve higher data rates while maintaining a lower BER over intra-body fading channels.
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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 | 0.000 | 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) | 0.000 | 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".