On the Ingleton-Violating Finite Groups and Group Network Codes

It is well known that there is a one-to-one correspondence between the entropy vector of a collection of n random variables and a certain group-characterizable vector obtained from a finite group and n of its subgroups [1]. However, if one restricts attention to abelian groups then not all entropy vectors can be obtained. This is an explanation for the fact shown by Dougherty et al [2] that linear network codes cannot achieve capacity in general network coding problems (since linear network codes form an abelian group). All abelian group-characterizable vector s, and by fiat all entropy vectors generated by linear network codes, satisfy a linear inequality called th e Ingleton inequality. In this paper, we study the problem of finding nonabelian finite groups that yield charac terizable vectors which violate the Ingleton inequality. Using a refined computer search, we find the symme tric group S5 to be the smallest group that violates the Ingleton inequality. Careful study of the stru cture of this group, and its subgroups, reveals that it belongs to the Ingleton-violating family PGL(2,p) with primes p ≥ 5, i.e., the projective group of 2×2 nonsingular matrices with entries in Fp. This family of groups is therefore a good candidate for constructing network codes more powerful than linear network codes.

[1]  M. Lunelli,et al.  Representation of matroids , 2002, math/0202294.

[2]  Nikolai K. Vereshchagin,et al.  Inequalities for Shannon Entropy and Kolmogorov Complexity , 1997, J. Comput. Syst. Sci..

[3]  Nigel Boston,et al.  Large violations of the Ingleton inequality , 2012, 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[4]  S. Shadbakht Entropy Region and Network Information Theory , 2011 .

[5]  Ryan Kinser,et al.  New inequalities for subspace arrangements , 2009, J. Comb. Theory, Ser. A.

[6]  Ho-Leung Chan,et al.  A combinatorial approach to information inequalities , 1999, 1999 Information Theory and Networking Workshop (Cat. No.99EX371).

[7]  Babak Hassibi,et al.  Violating the Ingleton inequality with finite groups , 2009, 2009 47th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[8]  Pirita Paajanen Finite p-Groups, Entropy Vectors, and the Ingleton Inequality for Nilpotent Groups , 2014, IEEE Transactions on Information Theory.

[9]  Randall Dougherty,et al.  Networks, Matroids, and Non-Shannon Information Inequalities , 2007, IEEE Transactions on Information Theory.

[10]  Randall Dougherty,et al.  Insufficiency of linear coding in network information flow , 2005, IEEE Transactions on Information Theory.

[11]  Randall Dougherty,et al.  Characteristic-dependent linear rank inequalities and network coding applications , 2014, 2014 IEEE International Symposium on Information Theory.

[12]  Babak Hassibi,et al.  MCMC methods for entropy optimization and nonlinear network coding , 2010, 2010 IEEE International Symposium on Information Theory.

[13]  B. Hassibi,et al.  Cayley's hyperdeterminant, the principal minors of a symmetric matrix and the entropy region of 4 Gaussian random variables , 2008, 2008 46th Annual Allerton Conference on Communication, Control, and Computing.

[14]  Zhen Zhang,et al.  On Characterization of Entropy Function via Information Inequalities , 1998, IEEE Trans. Inf. Theory.

[15]  F. Mattt,et al.  Conditional Independences among Four Random Variables Iii: Final Conclusion , 1999 .

[16]  Randall Dougherty,et al.  Non-Shannon Information Inequalities in Four Random Variables , 2011, ArXiv.

[17]  Alex J. Grant,et al.  Truncation Technique for Characterizing Linear Polymatroids , 2011, IEEE Transactions on Information Theory.

[18]  Hua Li,et al.  On Connections between Group Homomorphisms and the Ingleton Inequality , 2007, 2007 IEEE International Symposium on Information Theory.

[19]  Frantisek Matús,et al.  Conditional Independences among Four Random Variables II , 1995, Combinatorics, Probability and Computing.

[20]  T. Chan,et al.  Capacity regions for linear and abelian network codes , 2007, 2007 Information Theory and Applications Workshop.

[21]  Terence Chan,et al.  Group characterizable entropy functions , 2007, 2007 IEEE International Symposium on Information Theory.

[22]  Terence Chan On the optimality of group network codes , 2005, Proceedings. International Symposium on Information Theory, 2005. ISIT 2005..

[23]  Frédérique E. Oggier,et al.  Groups and information inequalities in 5 variables , 2013, 2013 51st Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[24]  Randall Dougherty,et al.  Linear rank inequalities on five or more variables , 2009, ArXiv.

[25]  Randall Dougherty Computations of linear rank inequalities on six variables , 2014, 2014 IEEE International Symposium on Information Theory.

[26]  Babak Hassibi,et al.  Normalized Entropy Vectors, Network Information Theory and Convex Optimization , 2007, 2007 IEEE Information Theory Workshop on Information Theory for Wireless Networks.

[27]  Raymond W. Yeung,et al.  On a relation between information inequalities and group theory , 2002, IEEE Trans. Inf. Theory.

[28]  Zhen Zhang,et al.  A non-Shannon-type conditional inequality of information quantities , 1997, IEEE Trans. Inf. Theory.

[29]  Babak Hassibi,et al.  On group network codes: Ingleton-bound violations and independent sources , 2010, 2010 IEEE International Symposium on Information Theory.

[30]  Milan Studený,et al.  Conditional Independences among Four Random Variables 1 , 1995, Comb. Probab. Comput..

[31]  H. O. Foulkes Abstract Algebra , 1967, Nature.