Bounds on the capacity of the additive inverse Gaussian noise channel

Chang, Huiting; Moser, Stefan M.

doi:10.1109/isit.2012.6284111

Cited by 9 publications

(19 citation statements)

References 6 publications

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“…1 is the optimized achievable rate as a function of average release time constraint M , where c = 1, λ = 1, W = 20 and x n = 0.1n, n = 0, · · · , 200. The optimized achievable rate is consistent with the upper bound and lower bounds provided in [1] and [3]. Evidently, the achievable rate given in this paper is close to the capacity.…”

Section: Methodssupporting

confidence: 85%

“…This is in contrast to the optimal input for the additive white Gaussian noise channel with both average power and peak power constraints [4] and the optimal inputs for a number of other channels [5]- [11], where the capacity-achievable input is discrete. The optimized achievable rates are consistent with existing upper and lower bounds [1], [3]. To the best of our knowledge, this work is the first recorded attempt to compute the capacity-achieving input distribution for the AIGN channel.…”

Section: Introductionsupporting

confidence: 80%

“…In a more recent paper [2], a closeform upper bound on the capacity of peak constrained AIGN channel is also provided. Another lower bound on the capacity of AIGN channel is given in [3].…”

Section: Introductionmentioning

confidence: 99%

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On the capacity-achieving input for additive inverse Gaussian channels

Guo

2013

2013 IEEE International Symposium on Information Theory

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In a molecular communication system, molecules convey the information by traversing from the transmitter to the receiver through the medium, which is often liquid. The time for a molecule to travel a fixed distance according to Brownian motion with a constant drift has the inverse Gaussian distribution. Hence the molecular communication channel is modeled by an additive inverse Gaussian noise channel, the input of which is the release times of the molecules. This paper studies the capacity-achieving input distribution for such a channel, where the release time is subject to both peak and average constraints. Several properties of the capacity-achieving input are established. A numerical method for computing the optimal input distribution is developed. The result complements some existing bounds on the capacity of molecular channel.

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Section: Methodssupporting

confidence: 85%

Section: Introductionsupporting

confidence: 80%

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On the capacity-achieving input for additive inverse Gaussian channels

Guo

2013

2013 IEEE International Symposium on Information Theory

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“…Although bounds are already well-established for the standard molecular timing channel, the synchronization error introduces new difficulties. In particular, as the receiver decodes based on Z = max{X + E T + N − E R , 0} (instead of X + N as in [6,7]), the standard bounds cannot be directly applied. We solve this problem next.…”

Section: Variance-constrained Capacitymentioning

confidence: 99%

“…In contrast to the standard AIGN timing channel model in [6,7], the timing channel with synchronization error must accommodate the information molecule arriving before the start of the receiver's time slot. As such, standard bounds on the capacity are not directly applicable.…”

mentioning

confidence: 99%

Variance-constrained capacity of the molecular timing channel with synchronization error

Egan

Deng

Elkashlan

et al. 2014

2014 IEEE Global Communications Conference

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Abstract-Molecular communication is set to play an important role in the design of complex biological and chemical systems. An important class of molecular communication systems is based on the timing channel, where information is encoded in the delay of the transmitted molecule-a synchronous approach. At present, a widely used modeling assumption is the perfect synchronization between the transmitter and the receiver. Unfortunately, this assumption is unlikely to hold in most practical molecular systems. To remedy this, we introduce a clock into the model-leading to the molecular timing channel with synchronization error. To quantify the behavior of this new system, we derive upper and lower bounds on the variance-constrained capacity, which we view as the step between the mean-delay and the peak-delay constrained capacity. By numerically evaluating our bounds, we obtain a key practical insight: the drift velocity of the clock links does not need to be significantly larger than the drift velocity of the information link, in order to achieve the variance-constrained capacity with perfect synchronization.

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