Optimal Energy Allocation for Cooperative Molecular Communication With Imperfect Transmitters in Internet of Bio-Nano Things
Bibliographic record
Abstract
Cooperative molecular communication (MC) is a key enabler for communication between nanomachines in the Internet of Bio-Nano Things (IoBNT). However, its performance is significantly constrained by the limited availability of free energy, which is essential for molecular transport. This paper introduces a novel transmitter model that encodes information by utilizing free energy to transport molecules from a reservoir to the external environment, creating specific concentration ratios in the reservoir for reliable information transmission. The transmitter’s performance is primarily influenced by energy consumption, which directly impacts the system’s bit error rate (BER) in IoBNT. To address these challenges, this study focuses on optimizing energy allocation among multiple transmitters in cooperative MC systems to enhance BER performance. For scenarios with two transmitters, a theoretical analysis of optimal energy allocation is performed, while reinforcement learning (RL) is utilized to determine optimal energy allocation strategies for systems with more than two transmitters. Numerical results demonstrate the effectiveness of the proposed strategies in minimizing BER and improving the overall performance of cooperative MC systems under energy constraints.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".