Tradeoff Between Energy Consumption and BER Performance in Molecular Communications
Bibliographic record
Abstract
In molecular communication (MC) systems, energy consumption plays a critical role in determining the bit error rate (BER) performance. This paper investigates an MC system with an imperfect transmitter that collects two types of molecules from the environment and releases them as a mixture. The receiver is equipped with receptors that selectively bind only to one target molecule type. The presence of non-target (interference) molecules in the transmitted mixture weakens the effective signal strength, thereby degrading detection accuracy. To mitigate this issue, the transmitter can selectively remove non-target molecules, enhancing the molecular purity of the transmitted signal. However, this purification process incurs additional energy consumption, introducing a fundamental trade-off between energy efficiency and communication reliability. To address this, we formulate a tradeoff function that jointly characterizes the energy consumption and BER performance. A grid search algorithm is then employed to identify the optimal energy allocation that minimizes the tradeoff function. Theoretical analysis and simulation results confirm the validity of the proposed framework, highlighting its utility in optimizing energy-efficient design for MC systems operating 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".