Computer Science > Emerging Technologies
[Submitted on 17 Sep 2026]
Title:A Closed-Form Molecule-Release Rule for Diffusion-Based Molecular Communications with Ligand Receptors
View PDF HTML (experimental)Abstract:The number of molecules released per bit is a fundamental design variable of diffusion-based molecular communication (MC), and ligand-receptor reception breaks the more-is-better intuition. Too few molecules leave the bound-receptor observations buried in binding noise, while too many amplify the accumulated intersymbol interference and saturate the finite receptor population, again making the observations indistinguishable. Reliability therefore peaks in an interior operating region whose location seems to require an exhaustive search over the channel dynamics. In this paper, we show that this search can be obviated for a biologically plausible receiver that compares consecutive bound-receptor counts without channel state information or a decision threshold. We derive a closed-form transmission rule, which sets the number of molecules released per bit such that the receptor dissociation constant equals the geometric mean of the two bit-conditioned received concentration levels, prove that it exactly minimizes the bit error probability of a memoryless binomial receptor model, and express it in the physical channel parameters through an Euler--Maclaurin evaluation of the interference. Time-domain Monte Carlo sweeps of the channel and receptor parameters, corroborated by particle-based simulations, show that the empirically optimal release count coincides with the prediction or lies above it by a small factor.
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Facts Only
* The number of molecules released per bit is a design variable in diffusion-based molecular communication (MC).
* Too few molecules result in observations being buried in binding noise.
* Too many molecules amplify accumulated intersymbol interference and saturate the finite receptor population.
* Reliability peaks in an interior operating region determined by channel dynamics.
* A closed-form transmission rule is derived where the receptor dissociation constant equals the geometric mean of bit-conditioned received concentration levels.
* This rule exactly minimizes the bit error probability for a memoryless binomial receptor model.
* The rule is expressed in physical channel parameters using an Euler--Maclaurin evaluation of interference.
* Time-domain Monte Carlo sweeps and particle-based simulations show the empirically optimal release count matches or is slightly above the prediction.
Executive Summary
Full Take
The core insight moves beyond empirical testing to establish a formal, mathematically derived rule for system optimization in molecular communication. The tension lies between the intuitive scaling of information transfer (more signal is better) and the physical limitations imposed by receptor saturation and noise accumulation. The derivation shifts the focus from an exhaustive search over complex channel dynamics to a single closed-form condition involving geometric means of received concentrations, which is highly valuable for practical system design.
The implication is that optimal performance in biological communication systems is governed by a delicate balance where signal intensity must be finely tuned against physical constraints (receptor binding and noise). The fact that this theoretically derived optimum aligns with empirical Monte Carlo results suggests a strong underlying invariance across the modeling assumptions, lending significant weight to the closed-form rule. The work suggests that understanding system performance requires explicitly incorporating receptor kinetics into the transmission protocol, rather than treating them as secondary observational effects. Future inquiry must focus on extending this framework to more complex, non-memoryless receptor models and incorporating dynamic environmental noise characteristics.
Sentinel — Human
This text reads like a genuine excerpt from a peer-reviewed scientific paper, exhibiting the complex synthesis expected of expert human authorship rather than typical synthetic pattern generation.