Maximum a posteriori-based molecular circuit demodulators for spatially partitioned molecular communication receivers
Bibliographic record
Abstract
This paper is built upon our earlier work on using a Markovian approach to design demodulators for diffusion-based molecular communications. In this earlier work, we show that demodulation can be performed by using a bank of analog filters, which are modelled by ordinary differential equations, to compute the log-posteriori probability that a transmission symbol is transmitted given the observations available to the demodulator. This earlier work was recently extended in two different ways. First, the earlier work assumes that the receiver is limited to a small volume called a voxel. We recently extended the work to the case where a receiver is modelled by a volume consisting of multiple voxels. In particular, we show that we can reduce the bit error rate by using receivers that are spatially partitioned. Second, the earlier work assumes that the computation of the log-posteriori probability is carried out in silico. We recently showed how the computation can be approximately carried out by a molecular circuit, i.e. a set of reactions. This paper builds on these two recent extensions. We make the following contributions. First, we derive molecular circuits to approximately compute the log-posteriori probability for partitioned receivers. We show how this is done for two different types of concentration modulation schemes. Second, the earlier work considered the demodulation of only one symbol. We extend the work so that the demodulator can decode a sequence of symbols by introducing a reset mechanism in the demodulator.
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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".