Channel Modeling for Diffusive Molecular Communication—A Tutorial Review

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This paper presents a stochastic analysis of the time-variant channel impulse response (CIR) of a three dimensional diffusive mobile molecular communication (MC) system where the transmitter, the absorbing receiver, and the molecules can freely diffuse. In our analysis, we derive the mean, variance, probability density function (PDF), and cumulative distribution function (CDF) of the CIR. We also derive the PDF and CDF of the probability p that a released molecule is absorbed at the receiver during a given time period.The obtained analytical results are employed for the design of drug delivery and MC systems with imperfect channel state information. For the first application, we exploit the mean and variance of the CIR to optimize a controlled-release drug delivery system employing a mobile drug carrier. We evaluate the performance of the proposed release design based on the PDF and CDF of the CIR. We demonstrate significant savings in the amount of released drugs compared to a constant-release scheme and reveal the necessity of accounting for the drug-carrier's mobility to ensure reliable drug delivery. For the second application, we exploit the PDF of the distance between the mobile transceivers and the CDF of p to optimize three design parameters of an MC system employing on-off keying modulation and threshold detection. Specifically, we optimize the detection threshold at the receiver, the release profile at the transmitter, and the time duration of a bit frame. We show that the proposed optimal designs can significantly improve the system performance in terms of the bit error rate and the efficiency of molecule usage.

“…For a given distance between Tx and Rx, rptq, the CIR hpt, τ q of a diffusive mobile MC system at time τ is given by [1], [2] hpt, τ q " a rx ?…”

Section: ) Time-variant Cirmentioning

confidence: 99%

Section: ) Time-variant Cirmentioning

confidence: 99%

Diffusive Mobile MC With Absorbing Receivers: Stochastic Analysis and Applications

Cao

Ahmadzadeh

Jamali

et al. 2019

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“…This assumption allows us to consider the propagation of these molecules to be independent of each other. The diffusion coefficient can be considered to be constant by assuming temperature and viscosity in the propagation environment as homogeneous and constant along with the above assumption [23].…”

Section: Propagation Modelmentioning

confidence: 99%

Analysis of Diffusion Based Molecular Communication With Multiple Transmitters Having Individual Random Information Bits

Sabu

Gupta

2019

In this paper, we present an analytical framework to derive the performance of a molecular communication system where a transmitter bio-nano-machine (TBN) is communicating with a fully-absorbing spherical receiver bio-nano-machine (RBN) in a diffusive propagation medium in the presence of other TBNs. We assume that transmit bits at each TBN is random and different than transmit bits at other TBNs. We model the TBNs using a marked Poisson point process (PPP) with their locations as points of PPP and transmit symbols as marks. We consider both inter-symbol interference (ISI) and co-channel interference (CCI). ISI is caused by molecules transmitted in the previous slots while CCI is due to the molecules emitted from other TBNs. We derive the bit error probability of this system by averaging over the distribution of the transmit bits as opposed to the past approaches consisting of conditioning on previous transmit bits and/or assuming the transmit bits of every TBN are the same. Using numerical results, we validate our analysis and provide various design insights about the system, for example, the impact of detection threshold on the system performance. We also show the importance of accurately incorporating the randomness of transmit bits in the analysis.

“…Stochastic simulation is used to verify the accuracy of Algorithm 1 for CR computation and the Poisson statistics in (5). We refer the readers to [15], [17], [19], [38] for an overview of general stochastic simulation of reaction-diffusion systems. In the following, we explain the particular particlebased simulator that is used for performance verification in this paper.…”

Section: Appendix F Stochastic Simulationmentioning

confidence: 99%

Diffusive Molecular Communications With Reactive Molecules: Channel Modeling and Signal Design

Jamali

Farsad

Schober

et al. 2018

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This paper focuses on molecular communication (MC) systems using two types of signaling molecules which may participate in a reversible bimolecular reaction in the channel. The motivation for studying these MC systems is that they can realize the concept of constructive and destructive signal superposition, which leads to favorable properties such as intersymbol interference (ISI) reduction and avoiding environmental contamination due to continuous release of signaling molecules into the channel. This work first presents a general formulation for binary modulation schemes that employ two types of signaling molecules and proposes several modulation schemes as special cases. Moreover, two types of receivers are considered: a receiver that is able to observe both types of signaling molecules (2TM), and a simpler receiver that can observe only one type of signaling molecules (1TM). For both of these receivers, the maximum likelihood (ML) detector for general binary modulation is derived under the assumption that the detector has perfect knowledge of the ISI-causing sequence. The performance of this genie-aided ML detector yields an upper bound on the performance of any practical detector. In addition, two suboptimal detectors of different complexity are proposed, namely an ML-based detector that employs an estimate of the ISI-causing sequence and a detector that neglects the effect of ISI. The proposed detectors, except for the detector that neglects ISI for the 2TM receiver, require knowledge of the channel response (CR) of the considered MC system. Moreover, the CR is needed for performance evaluation of all proposed detectors. However, deriving the CR of MC systems with reactive signaling molecules is challenging since the underlying partial differential equations that describe the reactiondiffusion mechanism are coupled and non-linear. Therefore, we develop an algorithm for efficient computation of the CR and validate its accuracy via particle-based simulation. Simulation results obtained using this CR computation algorithm confirm the effectiveness of the proposed modulation and detection schemes. Moreover, these results show that MC systems with reactive signaling have superior performance relative to those with nonreactive signaling due to the reduction of ISI enabled by the chemical reactions.