Performance Enhancement of Diffusion-Based Molecular Communication
Bibliographic record
Abstract
Inter-Symbol Interference (ISI) is one of the challenges of bio-inspired diffusion-based molecular communication. The degradation of the remaining molecules from a previous transmission is the solution that biological systems use to mitigate this ISI. While most prior work has proposed the use of enzymes to catalyze the molecules degradation, enzymes also degrade the molecules carrying the information, which drastically decreases the signal strength. In this paper, we propose the use of photolysis reactions, which use the light to instantly transform the emitted molecules so they no longer be recognized after their detection. The light will be emitted in an optimal time, allowing the receiver to detect as many molecules as possible, which increases both the signal strength and ISI mitigation. A lower bound expression on the expectation of the observed molecules number at the receiver is derived. Bit error probability expression is also formulated, and both expressions are validated with simulation results, which show a visible enhancement when using photolysis reactions. The performance of the proposed method is evaluated using Interference-to-Total-Received molecules metric (ITR) and the derived bit error probability.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".