A Comprehensive Analysis of Strength-Based Optimum Signal Detection in Concentration-Encoded Molecular Communication With Spike Transmission
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, a comprehensive analysis of strength-based optimum signal detection model has been presented for concentration-encoded molecular communication (CEMC) with spike (i.e., impulsive) transmission based on amplitude-shift keying (ASK) and on-off keying (OOK) modulations. Strength-based optimum signal detection problem in diffusion-based CEMC system has been investigated in detail in the presence of both diffusion noise and intersymbol interference (ISI). The receiver for optimum signal detection has been developed theoretically and explained with both analytical and simulation results of binary signal detection. Results show that the receiver thus developed can detect CEMC symbols effectively; however, the performance is influenced by three main factors, namely, communication range, transmission data rate, and receiver memory. For both ASK and OOK receivers, exact and approximate detection performances have been derived analytically depending on the probabilistic nature of molecular availability and the relationship between mean and variance of signal strengths. Correspondingly, bit error rate (BER) performance of the optimum receiver in a single CEMC link is further evaluated under various scenarios through extensive simulation experiments.
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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 | 0.003 |
| 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 it