CHARACTERIZATION OF MOLECULAR COMMUNICATION CHANNEL FOR NANOSCALE NETWORKS
Bibliographic record
Abstract
Recently molecular communication is being considered as a new communication physical layer option for nanonetworks. Nanonetworks are based on nanoscale artificial or bio-inspired nanomachines. Traditional communication technologies cannot work on the nanoscale because of the size and power consumption of transceivers and other components. On the other hand, a detailed knowledge of the molecular communication channel is necessary for successful communication. Some recent studies analyzed propagation impairment and its effects on molecular propagation. However, a proper characterization of the molecular propagation channel in nanonetworks is missing in the open literature. This goes without saying that a molecular propagation channel has to be characterized first before any performance evaluation can be made. Due to the nanoscale dimension of the nanomachines involved in molecular communication a measurement based approach using in vitro experiments is extremely difficult. In addition, a proper tuning of the experimental parameters is mandatory. This is why the authors were motivated to characterize the ‘channel quantum response (CQR)’ or equivalently the ‘throughput response’ of bio-inspired nanonetworks with an alternative approach. This paper considers the molecular channel as particle propagation. The CQR i.e. the throughput response and its characteristics have been found in order to better-understand the molecular channel behavior of nanonetworks.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".