Capacity of the Memoryless Additive Inverse Gaussian Noise Channel

Li, Hui; Moser, Stefan M.; Guo, Dongning

doi:10.1109/jsac.2014.2367673

Cited by 64 publications

(55 citation statements)

References 23 publications

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“…Observe from the figure that the approximation of t * in (14) is in good agreement with the solution in (9). In this regime, the duration the enzyme can be active while still ensuring coexistence is dependent only on the maximum perturbation ∆, the initial concentration of the enzyme E 0 , and the reaction rate coefficient k cat .…”

Section: Enzyme-aided Molecular Communicationssupporting

confidence: 66%

Coexistence in molecular communications

Egan¹,

Trang²,

Renzo³

2018

Nano Communication Networks

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Molecular communications is emerging as a technique to support coordination in nanonetworking, particularly in biochemical systems. In complex biochemical systems such as in the human body, it is not always possible to view the molecular communication link in isolation as chemicals in the system may react with chemicals used for the purpose of communication. There are two consequences: either the performance of the molecular communication link is reduced; or the molecular link disrupts the function of the biochemical system. As such, it is important to establish conditions when the molecular communication link can coexist with a biochemical system. In this paper, we develop a framework to establish coexistence conditions based on the theory of chemical reaction networks. We then specialize our framework in two settings: an enzyme-aided molecular communication system; and a low-rate molecular communication system near a biochemical system. In each case, we prove sufficient conditions to ensure coexistence.

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Section: Enzyme-aided Molecular Communicationssupporting

confidence: 66%

Coexistence in molecular communications

Egan¹,

Trang²,

Renzo³

2018

Nano Communication Networks

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“…Let T rls denote the vector containing the release times of the molecules by the transmitter and let T arv be the vector containing the respective arrival times of the molecules at the receiver. Thus, T arv is related to T rls according to [95], [96], [97], [98], [99], [100] T arv = T rls + T dly ,…”

Section: A Unified Signal Definitionmentioning

confidence: 99%

Channel Modeling for Diffusive Molecular Communication—A Tutorial Review

et al. 2019

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Molecular communication (MC) is a new communication engineering paradigm where molecules are employed as information carriers. MC systems are expected to enable new revolutionary applications such as sensing of target substances in biotechnology, smart drug delivery in medicine, and monitoring of oil pipelines or chemical reactors in industrial settings. As for any other kind of communication, simple yet sufficiently accurate channel models are needed for the design, analysis, and efficient operation of MC systems. In this paper, we provide a tutorial review on mathematical channel modeling for diffusive MC systems. The considered end-to-end MC channel models incorporate the effects of the release mechanism, the MC environment, and the reception mechanism on the observed information molecules. Thereby, the various existing models for the different components of an MC system are presented under a common framework and the underlying biological, chemical, and physical phenomena are discussed. Deterministic models characterizing the expected number of molecules observed at the receiver and statistical models characterizing the actual number of observed molecules are developed. In addition, we provide channel models for timevarying MC systems with moving transmitters and receivers, which are relevant for advanced applications such as smart drug delivery with mobile nanomachines. For complex scenarios, where simple MC channel models cannot be obtained from first principles, we investigate simulation-driven and experimentallydriven channel models. Finally, we provide a detailed discussion of potential challenges, open research problems, and future directions in channel modeling for diffusive MC systems.

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“…A5) Initially, there are no information particles in the environment and the particles that arrive at the receiver are the ones released by the transmitter. Note that these assumptions are typical in the study of molecular communication systems [20], [43]- [50].…”

Section: System Modelsmentioning

confidence: 99%

“…In this case the noise term follows the inverse Gaussian (IG) distribution. Capacity bounds for the additive IG noise channel, in bits per channel use, under an average delay constraint, were derived in [19], [20]. In [1], we have shown that in the case of timing-based modulation, where information is encoded on the release timing of particles, and pure diffusive transport (i.e., diffusion without any flow) is employed, the channel can be represented as an additive noise channel where the noise follows the Lévy distribution.…”

Section: Introductionmentioning

confidence: 99%

Communication System Design and Analysis for Asynchronous Molecular Timing Channels

Farsad

Murin

Guo

et al. 2017

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

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Two new asynchronous modulation techniques for molecular timing (MT) channels are proposed. One based on modulating information on the time between two consecutive releases of indistinguishable information particles, and one based on using distinguishable particles. For comparison, we consider the synchronized modulation scheme where information is encoded in the time of release and decoded from the time of arrival of particles. We show that all three modulation techniques result in a system that can be modeled as an additive noise channel, and we derive the expression for the probability density function of the noise. Next, we focus on binary communication and derive the associated optimal detection rules for each modulation. Since the noise associated with these modulations has an infinite variance, geometric power is used as a measure for the noise power, and we derive an expression for the geometric SNR (G-SNR) for each modulation scheme. Numerical evaluations indicate that for these systems the bit error rate (BER) is constant at a given G-SNR, similar to the relation between BER and SNR in additive Gaussian noise channels. We also demonstrate that the asynchronous modulation based on two distinguishable particles can achieve a BER performance close to the synchronized modulation scheme.

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Capacity of the Memoryless Additive Inverse Gaussian Noise Channel

Cited by 64 publications

References 23 publications

Coexistence in molecular communications

Coexistence in molecular communications

Channel Modeling for Diffusive Molecular Communication—A Tutorial Review

Communication System Design and Analysis for Asynchronous Molecular Timing Channels

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