ISI Mitigation Using Neural Networks in Molecular Communication With an Imperfect Transmitter Between Bionanosensors
Bibliographic record
Abstract
Diffusion-based molecular communication (DBMC) systems between bionanosensors face challenges from inter-symbol interference (ISI) and counting noise, due to channel memory and the random diffusion of molecules. This study focuses on a binary imperfect transmitter that exacerbates ISI originating from the transmitter itself, significantly complicating the decoding process. To address ISI and enhance system reliability, we propose detection methods based on convolutional neural network (CNN) and deep neural network (DNN), which operate without requiring prior knowledge of time-varying channel information. These methods are compared against K-means clustering and optimal detection based on the ratio of received molecules. Additionally, we investigate the impact of two types of inputs for CNN and DNN: coordinate input (e.g., the number of received molecules) and ratio input (e.g., the ratio of received molecules), and the impact of transmitter molecular concentration on system performance. The simulation results demonstrate that the coordinate input outperforms the ratio input in terms of detection accuracy. The performance of K-means clustering is shown to be comparable to optimal detection, while CNN, owing to their strong local feature perception capabilities, outperform DNN. Moreover, a larger concentration difference between the molecular reservoirs at the transmitter improves system performance. Overall, this study provides critical insights into the strengths and limitations of conventional and neural network-based detection techniques for mitigating ISI in MC systems, contributing to advancements in reliable DBMC system design.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".