Proceedings of the Eighth Workshop on Information Theoretic Methods in Science and Engineering

The capacity of optical fiber channels seems difficult to compute or even bound. The best capacity lower bounds are based on numerical simulations using the split-step Fourier method. We review a recent capacity upper bound that applies two basic tools to this method: maximum entropy under a correlation constraint and Shannon’s entropy power inequality (EPI). The main insight is that the non-linearity that is commonly used to model optical fiber propagation does not change the differential entropy of a signal. As a result, the spectral efficiency of fiber is at most log(1 + SNR), where SNR is the receiver signal-to-noise ratio. The results extend to other channels, including multi-mode fiber.

[1]  Jae Oh Woo,et al.  A lower bound on the Rényi entropy of convolutions in the integers , 2014, 2014 IEEE International Symposium on Information Theory.

[2]  H. Marko,et al.  The Bidirectional Communication Theory - A Generalization of Information Theory , 1973, IEEE Transactions on Communications.

[3]  Haim H. Permuter,et al.  Interpretations of Directed Information in Portfolio Theory, Data Compression, and Hypothesis Testing , 2009, IEEE Transactions on Information Theory.

[4]  Vadim A. Kaimanovich,et al.  Random Walks on Discrete Groups: Boundary and Entropy , 1983 .

[5]  Liyao Wang,et al.  Optimal Concentration of Information Content For Log-Concave Densities , 2015, ArXiv.

[6]  E. Lehmann Testing Statistical Hypotheses , 1960 .

[7]  V. Milman,et al.  Geometry of Log-concave Functions and Measures , 2005 .

[8]  Wojciech Szpankowski,et al.  Identifying Statistical Dependence in Genomic Sequences via Mutual Information Estimates , 2007, EURASIP J. Bioinform. Syst. Biol..

[9]  Mokshay M. Madiman,et al.  Sumset and Inverse Sumset Inequalities for Differential Entropy and Mutual Information , 2012, IEEE Transactions on Information Theory.

[10]  Hans S. Witsenhausen,et al.  A conditional entropy bound for a pair of discrete random variables , 1975, IEEE Trans. Inf. Theory.

[11]  Ioannis Kontoyiannis,et al.  Estimating the Directed Information and Testing for Causality , 2015, IEEE Transactions on Information Theory.

[12]  Tsachy Weissman,et al.  Rate-distortion in near-linear time , 2008, 2008 IEEE International Symposium on Information Theory.

[13]  Erwin Lutwak,et al.  Moment-entropy inequalities , 2004 .

[14]  C. Granger Investigating causal relations by econometric models and cross-spectral methods , 1969 .

[15]  Haim H. Permuter,et al.  Universal Estimation of Directed Information , 2010, IEEE Transactions on Information Theory.

[16]  K. Ball Logarithmically concave functions and sections of convex sets in $R^{n}$ , 1988 .

[17]  S. S. Wilks The Large-Sample Distribution of the Likelihood Ratio for Testing Composite Hypotheses , 1938 .

[18]  Sergey G. Bobkov,et al.  Dimensional behaviour of entropy and information , 2011, ArXiv.

[19]  Zaher Dawy,et al.  Genomic analysis using methods from information theory , 2004, Information Theory Workshop.

[20]  Alfred O. Hero,et al.  Using Directed Information to Build Biologically Relevant Influence Networks , 2007, J. Bioinform. Comput. Biol..

[21]  Mokshay M. Madiman,et al.  Entropies of Weighted Sums in Cyclic Groups and an Application to Polar Codes , 2017, Entropy.

[22]  A. Lapidoth,et al.  On the entropy of the sum and of the difference of independent random variables , 2008, 2008 IEEE 25th Convention of Electrical and Electronics Engineers in Israel.

[23]  Donald L. Iglehart,et al.  Importance sampling for stochastic simulations , 1989 .

[24]  C. Borell Convex measures on locally convex spaces , 1974 .

[25]  Olivier J. J. Michel,et al.  The relation between Granger causality and directed information theory: a review , 2012, Entropy.

[26]  Gerhard Kramer,et al.  Directed information for channels with feedback , 1998 .

[27]  Todd P. Coleman,et al.  Estimating the directed information to infer causal relationships in ensemble neural spike train recordings , 2010, Journal of Computational Neuroscience.