Hybrid CNN-BiLSTM for ISI Mitigation in Molecular Communication for Nanosensors with Imperfect Transmitter
Bibliographic record
Abstract
Inter-symbol interference (ISI) mitigation is a critical challenge in diffusion-based molecular communication systems for nanosensors due to the inherent channel memory at the nanoscale. In the presence of an imperfect transmitter, the emitted molecules include interference components, further complicating signal decoding at the receiver. Traditional ISI mitigation techniques often rely on precise channel state information, which is time-varying and computationally demanding. To address this issue, we propose a hybrid deep learning model, C-BiLSTM, which integrates convolutional neural networks (CNN) for local feature extraction and bidirectional long short-term memory (BiLSTM) networks for capturing long-range dependencies. The model is trained and decoded using received time-window sequences to enhance detection performance. Simulation results demonstrate that C-BiLSTM achieves a lower bit error rate (BER) than CNN alone and approaches the optimal detection performance. These findings highlight the potential of deep learning-based approaches for ISI mitigation, offering a promising solution for enhancing reliability in molecular communication systems.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".