An asymptotic expression for the information and capacity of a multidimensional channel with weak input signals

Prelov, V. V.; Meulen, Edward C. van der

doi:10.1109/18.259667

Cited by 38 publications

(44 citation statements)

References 5 publications

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“…Moreover,x has covariance matrixQ * = 1 σ 2 Q * with componentsq * i,j . This channel satisfies conditions A-F in [28], and hence [28, Theorem 1] applies. Hence, the achievable rate is given by…”

Section: Proof Of Theoremmentioning

confidence: 99%

“…Proof: The statement follows using Theorem 1, applying results in [28] to (14), and using the definition of C [2] σ,0 (A, E) given in Sec. II.…”

Section: B Fixed Peak Snrmentioning

confidence: 99%

“…The achievability follows using results in [28] to evaluate R * σ (A, E) at asymptotically low SNR and is detailed in Sec. IV-C. Another proof that relies on the relation between the mutual information and the minimum mean-square error over a Gaussian channel [30] is given in Appendix B.…”

Section: A Proportional Average and Peak Snrsmentioning

confidence: 99%

“…To name a few, this has been studied earlier in [28] which provides capacity expressions under "weak" input signals, in [29] which focuses on low-SNR second order asymptotics of channel capacity, and in [30] which discusses low SNR asymptotics using the relation between mutual information and minimum mean square error (MMSE) over Gaussian channels. In this context, the contribution of this work is characterizing the low-SNR asymptotic capacity of MIMO IM-DD channels and shedding light on the capacity achieving input in this regime.…”

Section: Introductionmentioning

confidence: 99%

“…III). The achievable rate is derived using results in [28]. This leads to the low-SNR asymptotic capacity under a total average intensity constraint for two cases: (i) proportionally vanishing average and peak constraints, and (ii) fixed peak constraint and vanishing average constraint.…”

Section: Introductionmentioning

confidence: 99%

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Low-SNR Asymptotic Capacity of MIMO Optical Intensity Channels with Peak and Average Constraints

Chaaban

Rezki

Alouini

2018

IEEE Trans. Commun.

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Abstract-The low-SNR asymptotic capacity of the multipleinput multiple-output (MIMO) optical intensity channel is studied under both average and peak intensity constraints. We focus on low SNR, which can be modeled as the scenario where both constraints proportionally vanish, or where the peak constraint is held constant while the average constraint vanishes. A capacity upper bound is derived, and is shown to be tight at low SNR under both scenarios. The capacity achieving input distribution at low SNR is shown to be a maximally-correlated vectorbinary input distribution. Consequently, the low-SNR capacity of the channel is characterized. As a byproduct, it is shown that for a channel with peak intensity constraints only, or with peak intensity constraints and individual (per aperture) average intensity constraints, a simple scheme composed of coded onoff keying, spatial repetition, and maximum-ratio combining is optimal at low SNR.

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Section: Proof Of Theoremmentioning

confidence: 99%

“…Proof: The statement follows using Theorem 1, applying results in [28] to (14), and using the definition of C [2] σ,0 (A, E) given in Sec. II.…”

Section: B Fixed Peak Snrmentioning

confidence: 99%

Section: A Proportional Average and Peak Snrsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Low-SNR Asymptotic Capacity of MIMO Optical Intensity Channels with Peak and Average Constraints

Chaaban

Rezki

Alouini

2018

IEEE Trans. Commun.

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Higher-Order Asymptotics of Mutual Information for Nonlinear Channels with Non-Gaussian Noise

Prelov

Meulen

2003

Problems of Information Transmission

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Capacity of very noisy communication channels based on Fisher information

Duan¹,

Chapeau‐Blondeau²,

Abbott³

2016

Sci Rep

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We generalize the asymptotic capacity expression for very noisy communication channels to now include coloured noise. For the practical scenario of a non-optimal receiver, we consider the common case of a correlation receiver. Due to the central limit theorem and the cumulative characteristic of a correlation receiver, we model this channel noise as additive Gaussian noise. Then, the channel capacity proves to be directly related to the Fisher information of the noise distribution and the weak signal energy. The conditions for occurrence of a noise-enhanced capacity effect are discussed, and the capacity difference between this noisy communication channel and other nonlinear channels is clarified.

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An asymptotic expression for the information and capacity of a multidimensional channel with weak input signals

Cited by 38 publications

References 5 publications

Low-SNR Asymptotic Capacity of MIMO Optical Intensity Channels with Peak and Average Constraints

Low-SNR Asymptotic Capacity of MIMO Optical Intensity Channels with Peak and Average Constraints

Higher-Order Asymptotics of Mutual Information for Nonlinear Channels with Non-Gaussian Noise

Capacity of very noisy communication channels based on Fisher information

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