A Comprehensive Study of Sampling-Based Optimum Signal Detection in Concentration-Encoded Molecular Communication
Bibliographic record
Abstract
In this paper, a comprehensive analysis of the sampling-based optimum signal detection in ideal (i.e., free) diffusion-based concentration-encoded molecular communication (CEMC) system has been presented. A generalized amplitude-shift keying (ASK)-based CEMC system has been considered in diffusion-based noise and intersymbol interference (ISI) conditions. Information is encoded by modulating the amplitude of the transmission rate of information molecules at the TN. The critical issues involved in the sampling-based receiver thus developed are addressed in detail, and its performance in terms of the number of samples per symbol, communication range, and transmission data rate is evaluated. ISI produced by the residual molecules deteriorates the performance of the CEMC system significantly, which further deteriorates when the communication range and/or the transmission data rate increase(s). In addition, the performance of the optimum receiver depends on the receiver's ability to compute the ISI accurately, thus providing a trade-off between receiver complexity and achievable bit error rate (BER). Exact and approximate detection performances have been derived. Finally, it is found that the sampling-based signal detection scheme thus developed can be applied to both binary and multilevel (M-ary) ASK-based CEMC systems, although M-ary systems suffer more from higher BER.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".