Design Optimization of a MIMO Receiver for Diffusion-based Molecular Communication
Bibliographic record
Abstract
Path loss is a main challenge in Molecular Communications. When molecules carry information based only on a natural diffusion, the number of molecules that can be received is inversely proportional to the square distance between the transmitter and the receiver, thus hugely impacting the received signal strength. The use of a Multi-Input Multi-Output (MIMO) technique can improve the performance of molecular communications by increasing the data rate. In this paper, we studied the receiver used in molecular MIMO communications. We focused on three important parameters for the receiver design, which are the channel distance, the distance between the detectors constructing the receiver and the detectors diameter. To optimize the design of a 3×3 MIMO receiver, we used AcCoRD simulator to obtain 3D stochastic simulations for each scenario. We evaluated the simulation results by studying the error probability and the number of molecules representing the signal strength. We then proposed two optimization problems that aim at optimizing the receiver parameters choice, and two algorithms to solve the problems. The study shows that a judicious choice of the three parameters combination can optimize MIMOs receiver design, which can decrease the error probability and improve the performance of Molecular Communication.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".