Entropy and channel capacity in the regenerative setup with applications to Markov channels

Sharma, Vinod; Singh, Sandeep

doi:10.1109/isit.2001.936146

Cited by 79 publications

(56 citation statements)

References 2 publications

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“…Since p(y n 1 |x n 1 ) 6 = Q n k=1 p(y k |x k ), because of the dependence of the output on the previous inputs, a different method for the computation of the conditional pdf has to be considered. We now propose a continuous-value recursive computation of p(y n 1 |x n 1 ) similar to that which leads to (9). To the best of our knowledge, this method is a novel alternative for the symbol time model.…”

Section: B Upper Boundmentioning

confidence: 99%

On the information rate of phase noise-limited communications

Martalò

Tripodi

Raheli

2013

2013 Information Theory and Applications Workshop (ITA)

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Abstract-We discuss the accuracy of the time-discrete phase noise model described by a random walk with symbol-period spaced Gaussian increments. While this model is widely used for its simplicity, it is strictly valid in the slow phase regime only. It is customary to consider this model as a worst case, but a clear understanding of the phase dynamics which can be reliably represented may not be readily available.We address this problem by comparing the symbol-period spaced model with fractional-period spaced ones, which more reliably describe the physical system, in terms of the achievable information rate of the resulting phase noise channels. We show that indeed the symbol-period spaced model is a worst case that may offer a good modeling accuracy under a wide range of phase dynamics.

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Section: B Upper Boundmentioning

confidence: 99%

On the information rate of phase noise-limited communications

Martalò

Tripodi

Raheli

2013

2013 Information Theory and Applications Workshop (ITA)

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“…al. [20], and Sharma and Singh [21]. The connection between the entropy rate of a hidden Markov chain and the top Lyapunov exponent of a random matrix product has been observed [11,12,10].…”

Section: Introductionmentioning

confidence: 96%

Asymptotics of Noisy Constrained Channel Capacity

Han

Marcus

2007

2007 IEEE International Symposium on Information Theory

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“…In our analysis we used [h 0 , h 1 , h 2 , h 3 ] = [+0.5, +0.5, −0.5, −0.5], which is also known as the EPR4 5 As noted earlier, the lower bound is invariant to the multiplication of W (y|b) by a positive function of y.…”

Section: Numerical Resultsmentioning

confidence: 99%

Optimizing Information Rate Bounds for Channels with Memory

Sadeghi

Vontobel

Shams

2007

2007 IEEE International Symposium on Information Theory

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Abstract-We consider the problem of optimizing information rate upper and lower bounds for communication channels with (possibly large) memory. A recently proposed auxiliary-channelbased technique allows one to efficiently compute upper and lower bounds on the information rate of such channels. Towards tightening these bounds, we propose iterative expectationmaximization (EM) type algorithms to optimize the parameters of the auxiliary finite-state machine channel (FSMC). From a channel coding perspective, optimizing the lower bound is related to increasing the achievable mismatched information rate, i.e. the information rate of a communication system where the maximum-likelihood decoder at the receiver is matched to the auxiliary channel and not to the true channel. We provide explicit solutions for optimizing the upper bound and the difference between the upper and the lower bound and we discuss a method for the optimization of the lower bound for data-controllable channels with memory. We discuss examples of channels with memory, for which application of the developed theory results in noticeably tighter information rate bounds.

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Entropy and channel capacity in the regenerative setup with applications to Markov channels

Cited by 79 publications

References 2 publications

On the information rate of phase noise-limited communications

On the information rate of phase noise-limited communications

Asymptotics of Noisy Constrained Channel Capacity

Optimizing Information Rate Bounds for Channels with Memory

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