Capacity of Diffusion-Based Molecular Communication Networks Over LTI-Poisson Channels

Aminian, Gholamali; Arjmandi, Hamidreza; Gohari, Amin; Nasiri-Kenari, Masoumeh; Mitra, Urbashi

doi:10.1109/tmbmc.2015.2502858

Cited by 60 publications

(70 citation statements)

References 47 publications

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“…In this work, we incorporate the optimization of the symbol duration into the formulation of capacity, to present the channel capacity of the memoryless PIC in bits per second. This is one important distinction between this and previous work [2], [3], [10], [11] where the channel capacity is typically defined in bits per channel use.…”

Section: Imperfect Receivermentioning

confidence: 99%

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Capacity of molecular channels with imperfect particle-intensity modulation and detection

Farsad

Rose

Médard³

et al. 2017

2017 IEEE International Symposium on Information Theory (ISIT)

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Abstract-This work introduces the particle-intensity channel (PIC) as a model for molecular communication systems and characterizes the properties of the optimal input distribution and the capacity limits for this system. In the PIC, the transmitter encodes information, in symbols of a given duration, based on the number of particles released, and the receiver detects and decodes the message based on the number of particles detected during the symbol interval. In this channel, the transmitter may be unable to control precisely the number of particles released, and the receiver may not detect all the particles that arrive. We demonstrate that the optimal input distribution for this channel always has mass points at zero and the maximum number of particles that can be released. We then consider diffusive particle transport, derive the capacity expression when the input distribution is binary, and show conditions under which the binary input is capacity-achieving. In particular, we demonstrate that when the transmitter cannot generate particles at a high rate, the optimal input distribution is binary.

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Section: Imperfect Receivermentioning

confidence: 99%

“…Using this model, bounds on the capacity are derived, and the capacity for different memory lengths is analyzed. Reference [11] assumes that the channel input is the rate of particle release. The channel is represented as a Poisson channel with finite memory, and upper and lower bounds on capacity per channel use are presented.…”

Section: Introductionmentioning

confidence: 99%

Capacity of molecular channels with imperfect particle-intensity modulation and detection

Farsad

Rose

Médard³

et al. 2017

2017 IEEE International Symposium on Information Theory (ISIT)

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“…In the next subsection, we present the optimal VD, 3 Note that η is the noise term that is typically used in the Poisson channel model. In the optical communication literature this noise is also known as the dark current [36]- [38]. The noise is due to imperfect receiver, or background noise (due to ambient optical noise or molecules that may exist in the environment).…”

Section: The Poisson Channel Modelmentioning

confidence: 99%

Neural Network Detection of Data Sequences in Communication Systems

Farsad

Goldsmith

2018

IEEE Trans. Signal Process.

288

155

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We consider detection based on deep learning, and show it is possible to train detectors that perform well without any knowledge of the underlying channel models. Moreover, when the channel model is known, we demonstrate that it is possible to train detectors that do not require channel state information (CSI). In particular, a technique we call a sliding bidirectional recurrent neural network (SBRNN) is proposed for detection where, after training, the detector estimates the data in realtime as the signal stream arrives at the receiver. We evaluate this algorithm, as well as other neural network (NN) architectures, using the Poisson channel model, which is applicable to both optical and molecular communication systems. In addition, we also evaluate the performance of this detection method applied to data sent over a molecular communication platform, where the channel model is difficult to model analytically. We show that SBRNN is computationally efficient, and can perform detection under various channel conditions without knowing the underlying channel model. We also demonstrate that the bit error rate (BER) performance of the proposed SBRNN detector is better than that of a Viterbi detector with imperfect CSI as well as that of other NN detectors that have been previously proposed. Finally, we show that the SBRNN can perform well in rapidly changing channels, where the coherence time is on the order of a single symbol duration.

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“…This difficulty is addressed by the technique of "input distributions that escape to infinity", under some assumptions about the peak constraint. In this part, we give an upper bound based on the KL symmetrized upper bound of [15]. The idea is that…”

Section: An Upper Boundmentioning

confidence: 99%

On the Capacity of a Class of Signal-Dependent Noise Channels

Ghourchian

Aminian

Gohari

et al. 2018

IEEE Trans. Inform. Theory

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In some applications, the variance of additive measurement noise depends on the signal that we aim to measure. For instance, additive Gaussian signal-dependent noise (AGSDN) channel models are used in molecular and optical communication. Herein we provide lower and upper bounds on the capacity of additive signal-dependent noise (ASDN) channels. The idea of the first lower bound is the extension of the majorization inequality, and for the second one, it uses some calculations based on the fact that h (Y ) > h (Y |Z). Both of them are valid for all additive signal-dependent noise (ASDN) channels defined in the paper. The upper bound is based on a previous idea of the authors ("symmetric relative entropy") and is used for the additive Gaussian signal-dependent noise (AGSDN) channels. These bounds indicate that in ASDN channels (unlike the classical AWGN channels), the capacity does not necessarily become larger by making the variance function of the noise smaller. We also provide sufficient conditions under which the capacity becomes infinity. This is complemented by a number of conditions that imply capacity is finite and a unique capacity achieving measure exists (in the sense of the output measure).

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Capacity of Diffusion-Based Molecular Communication Networks Over LTI-Poisson Channels

Cited by 60 publications

References 47 publications

Capacity of molecular channels with imperfect particle-intensity modulation and detection

Capacity of molecular channels with imperfect particle-intensity modulation and detection

Neural Network Detection of Data Sequences in Communication Systems

On the Capacity of a Class of Signal-Dependent Noise Channels

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