Enhancing Signal Strength and ISI-Avoidance of Diffusion-based Molecular Communication
Bibliographic record
Abstract
Diffusion-based Molecular Communication is a bioinspired system, which uses random walk diffusive molecules as carriers of the information between the transmitter and the receiver. One of the main challenges of that system is the Inter-Symbol-Interference (ISI), caused by the channel memory and represented by a heavy tail in the impulse response. While most prior work has proposed the use of enzymes to catalyze the degradation of the remaining molecules, which mitigates ISI and increases the data rate, the enzymes will decrease the signal strength by degrading also the molecules carrying the information. In this paper, we propose the use of non-enzymatic reactions to degrade only the received molecules, which increases the amplitude of the received signal and at the same time mitigates ISI, enhancing by that the signal strength and the achievable throughput. In this study, we focused on photolysis reactions, which use light to instantly degrade the molecules. We studied the optimal time of light emission with 3D stochastic simulations, using AcCoRD simulator. Simulation results show an improvement of the received signal when using non-enzymatic reactions, compared to enzymatic systems. The performance of the proposed method was evaluated using interference-to-total-received molecules (ITR).
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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".