Statistical Physics of Random Binning

We consider the model of random binning and finite-temperature decoding for Slepian–Wolf codes, from a statistical–mechanical perspective. While ordinary random channel coding is intimately related to the random energy model—a statistical–mechanical model of disordered magnetic materials, it turns out that random binning (for Slepian–Wolf coding) is analogous to another, related statistical–mechanical model of strong disorder, which we call the random dilution model. We use the latter analogy to characterize phase transitions pertaining to finite-temperature Slepian–Wolf decoding, which are somewhat similar, but not identical, to those of finite-temperature channel decoding. We then provide the exact random coding exponent of the bit error rate as a function of the coding rate and the decoding temperature, and discuss its properties. Finally, a few modifications and extensions of our results are outlined and discussed.

[1]  R. Gallager Information Theory and Reliable Communication , 1968 .

[2]  Imre Csiszár Linear codes for sources and source networks: Error exponents, universal coding , 1982, IEEE Trans. Inf. Theory.

[3]  M. Mézard,et al.  Information, Physics, and Computation , 2009 .

[4]  Neri Merhav Statistical Physics of Random Binning , 2015, IEEE Transactions on Information Theory.

[5]  Neri Merhav,et al.  Statistical Physics and Information Theory , 2010, Found. Trends Commun. Inf. Theory.

[6]  Ruján Finite temperature error-correcting codes. , 1993, Physical review letters.

[7]  Neri Merhav Erasure/List Exponents for Slepian-Wolf Decoding , 2014, IEEE Trans. Inf. Theory.

[8]  Aaron B. Wagner,et al.  Improved Source Coding Exponents via Witsenhausen's Rate , 2011, IEEE Transactions on Information Theory.

[9]  Neri Merhav,et al.  Optimum Tradeoffs Between the Error Exponent and the Excess-Rate Exponent of Variable-Rate Slepian–Wolf Coding , 2015, IEEE Transactions on Information Theory.

[10]  Imre Csiszár,et al.  Graph decomposition: A new key to coding theorems , 1981, IEEE Trans. Inf. Theory.

[11]  Neri Merhav,et al.  Relations Between Random Coding Exponents and the Statistical Physics of Random Codes , 2007, IEEE Transactions on Information Theory.

[12]  Neri Merhav Exact Random Coding Error Exponents of Optimal Bin Index Decoding , 2014, IEEE Transactions on Information Theory.

[13]  Imre Csiszár,et al.  Towards a general theory of source networks , 1980, IEEE Trans. Inf. Theory.

[14]  Yoshiyuki Kabashima,et al.  Statistical mechanics of source coding with a fidelity criterion , 2005 .

[15]  Jack K. Wolf,et al.  Noiseless coding of correlated information sources , 1973, IEEE Trans. Inf. Theory.

[16]  Te Sun Han,et al.  Universal coding for the Slepian-Wolf data compression system and the strong converse theorem , 1994, IEEE Trans. Inf. Theory.

[17]  Neri Merhav,et al.  Codeword or Noise? Exact Random Coding Exponents for Joint Detection and Decoding , 2014, IEEE Transactions on Information Theory.

[18]  Yoshiyuki Kabashima,et al.  Statistical Mechanical Approach to Error Exponents of Lossy Data Compression , 2003 .