Localization of a Nano-transmitter in a Diffusive MC System with Multiple Fully-absorbing Receivers

Vakilipoor, Fardad; Magarini, Maurizio

doi:10.1109/percomworkshops53856.2022.9767415

Cited by 5 publications

(3 citation statements)

References 15 publications

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“…For every point on the grid, we locate the unknown receiver and compute the asymptotic expected number of absorbed molecules by the receivers in the environment (i.e. known and unknown receivers) by using (6). In practice, the number of absorbed molecules by the receivers follows a Binomial distribution due to the independent molecule behavior assumption and since any given molecule released by the transmitter is either absorbed by the receiver or not…”

Section: Localization Of the Unknown Receivermentioning

confidence: 99%

“…Creating the g i functions is equivalent to the concept of interference cancellation because thanks to the full knowledge of the position of the known receivers and the analytical model, the PDF maps corresponding to each known receiver become independent of the PDF associated with the other known receivers. The reason behind the independence of the PDFs is that all the information concerning the effect of other known receivers has already been taken into account in the PDF map due to the channel model (6). Hence, we can multiply the PDF maps by one another due to their independence.…”

Section: Localization Of the Unknown Receivermentioning

confidence: 99%

“…Localization in MC literature is a recent subject of research [5], [6]. In a network of multiple FA receivers it is often needed to localize a receiver.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Localizing the Unknown Receiver in a Diffusive SIMO Molecular Communication System

Vakilipoor

Ansari

Magarini

2023

IEEE Trans. Mol. Biol. Multi-Scale Commun.

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This paper introduces a method to estimate the position of an unknown fully absorbing (FA) spherical receiver in a diffusive molecular communication (MC) system. This latter consists of a point transmitter and multiple FA spherical receivers in a three-dimensional space. The method relies on a temporal asymptotic single-input multiple-output (SIMO) channel model for diffusive MC systems that takes into account the interaction among receivers. By relying on the asymptotic model, a gridbased likelihood function is a priori created from each known receiver's perspective. The likelihood function's computation requires information about the number of molecules released by the transmitter, its position, and the position of known receivers. From the cumulative number of molecules absorbed by the known FA receivers, different likelihood maps are obtained, where each map corresponds to the likelihood of the position of unknown receiver from a specific known receiver's perspective. By combining the receivers' likelihood maps, which is equivalent to cooperation by sharing their knowledge, the position of the unknown receiver is estimated. The quality of the estimation is evaluated in terms of root mean squared error (RMSE) between the estimated position of the unknown receiver and its true position for different number of released molecules.

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Section: Localization Of the Unknown Receivermentioning

confidence: 99%

Section: Localization Of the Unknown Receivermentioning

confidence: 99%