Nanomachine Localization in a Diffusive Molecular Communication System

Kumar, Sudhir

doi:10.1109/jsyst.2019.2963790

Cited by 27 publications

(14 citation statements)

References 14 publications

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“…2) Maximum Likelihood (ML) Estimation: The ML distance estimation is to find d that maximizes the joint observation likelihood. [19], [36] considered x m , i.e., the mth data of the data set x, as the number of IMs observed at the transparent RX at time t m when IMs are released at time t 0 . It is assumed that each observation is independent and follows a Poisson distribution.…”

Section: B One-way Estimationmentioning

confidence: 99%

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A Survey on Estimation Schemes in Molecular Communications

Huang,

Fang,

Yang

2021

Preprint

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This survey paper focuses on the estimation schemes in molecular communications (MC) systems. The existing studies in estimation schemes can be divided into parameter estimation (e.g., distance, diffusion coefficient, and flow velocity) and channel estimation. In this paper, we present, for the first time, a comprehensive survey on i) distance estimation, since distance is the most widely estimated parameter in current studies, ii) estimation of other parameters (i.e. the parameters excluding distance), and iii) channel estimation that focuses on the channel impulse response (CIR). Moreover, we examine the noise that may impact on the estimation performance and the metrics applied to evaluate the performance of different estimation schemes. Numerical results are provided to compare the performance of different distance estimation schemes. In addition, future research directions in parameter estimation and channel estimation are identified and discussed.

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Section: B One-way Estimationmentioning

confidence: 99%

“…It is noted that [19], [36] only considered the transmission of one symbol from the TX to the RX. Different from that, [37] considered multiple symbols transmitted from the TX to the transparent RX, where ISI exists and may impact the estimation performance.…”

Section: B One-way Estimationmentioning

confidence: 99%

A Survey on Estimation Schemes in Molecular Communications

Huang,

Fang,

Yang

2021

Preprint

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“…This step is crucial to realize the accurate location of the abnormality, in order to eliminate the adverse and side effects of drug on the other normal cells and to enhance the resolution of abnormality before the surgery and other therapies [7]- [9]. Some examples are localizing the unhealthy cells around the capillaries [10], guiding the nanorobots in the human body to the target sites needing treatment [5], [11], [12], and drug delivery, where the drug can be bounded to the mobile nanomachines to be delivered at the target location [9], [13], [14].…”

Section: Introductionmentioning

confidence: 99%

Joint Sensing, Communication and Localization of a Silent Abnormality Using Molecular Diffusion

Khaloopour¹,

Mirmohseni²,

Nasiri-Kenari³

2022

Preprint

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In this paper, we propose a molecular communication system to localize an abnormality in a diffusion based medium. We consider a general setup to perform joint sensing, communication and localization. This setup consists of three types of devices, each for a different task: mobile sensors for navigation and molecule releasing (for communication), fusion centers (FC)s for sampling, amplifying and forwarding the signal, and a gateway for making decision or exchanging the information with an external device. The sensors move randomly in the environment to reach the abnormality. We consider both collaborative and non-collaborative sensors that simultaneously release their molecules to the FCs when the number of activated sensors or the moving time reach a certain threshold, respectively. The FCs amplify the received signal and forward it to the gateway for decision making using either an ideal or a noisy communication channel. A practical application of the proposed model is drug delivery in a tissue of human body, in order to guide the nanomachine bound drug to the exact location. The decision rules and probabilities of error are obtained for two considered sensors types in both ideal and noisy communication channels.

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“…The aforementioned time instant at which the peak occurs is referred to as the peak time, which is also considered to be the time taken by a signal emitted at the transmitter to reach the receiver [2]. Besides decoding purposes, knowledge of the peak time is useful to estimate channel parameters [6]- [10] and also to perform networking functions [11].…”

Section: Introductionmentioning

confidence: 99%

Probability Distribution of a Signal’s Peak Time in a Molecular Diffusive Media

Shitiri

Yilmaz

Cho³

2021

IEEE Commun. Lett.

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The peak time of a received signal in a molecular channel is random and generally assumed to follow the Normal distribution. However, the untested validity of the assumption motivates this letter. Considering an absorbing receiver placed in a free-diffusion molecular communication via diffusion (MCvD) system, we analyze the randomness in terms of the distribution of the peak time using simulated data and define one of the parameters. In addition, we derive the semi-analytical expressions of the distribution by using Poisson and Normal approximations of the absorbed molecules. Through numerical analysis, we evaluate the accuracy of the models and demonstrate that the Poisson model achieves a better fit. The key finding is that the distribution has extreme values resulting in a heavy right-tail and this information can be a useful tool to develop improved channel models.

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Nanomachine Localization in a Diffusive Molecular Communication System

Cited by 27 publications

References 14 publications

A Survey on Estimation Schemes in Molecular Communications

A Survey on Estimation Schemes in Molecular Communications

Joint Sensing, Communication and Localization of a Silent Abnormality Using Molecular Diffusion

Probability Distribution of a Signal’s Peak Time in a Molecular Diffusive Media

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