Lossless Source Coding in the Point-to-Point, Multiple Access, and Random Access Scenarios

This work studies point-to-point, multiple access, and random access lossless source coding in the finite-blocklength regime. In each scenario, a random coding technique is developed and used to analyze third-order coding performance. Asymptotic results include a third-order characterization of the Slepian-Wolf rate region with an improved converse that relies on a connection to composite hypothesis testing. For dependent sources, the result implies that the independent encoders used by Slepian-Wolf codes can achieve the same third-order-optimal performance as a single joint encoder. The concept of random access source coding is introduced to generalize multiple access (Slepian-Wolf) source coding to the case where encoders decide independently whether or not to participate and the set of participating encoders is unknown a priori to both the encoders and the decoder. The proposed random access source coding strategy employs rateless coding with scheduled feedback. A random coding argument proves the existence of a single deterministic code of this structure that simultaneously achieves the third-order-optimal Slepian-Wolf performance for each possible active encoder set.

[1]  Vincent Y. F. Tan,et al.  On the dispersions of three network information theory problems , 2012, 2012 46th Annual Conference on Information Sciences and Systems (CISS).

[2]  Yury Polyanskiy,et al.  A perspective on massive random-access , 2017, 2017 IEEE International Symposium on Information Theory (ISIT).

[3]  Pierre Moulin,et al.  Strong large deviations for composite hypothesis testing , 2014, 2014 IEEE International Symposium on Information Theory.

[4]  F. Kanaya,et al.  Coding Theorems on Correlated General Sources , 1995 .

[5]  Yasutada Oohama Universal coding for correlated sources with linked encoders , 1996, IEEE Trans. Inf. Theory.

[6]  Masahito Hayashi,et al.  Second-Order Asymptotics in Fixed-Length Source Coding and Intrinsic Randomness , 2005, IEEE Transactions on Information Theory.

[7]  Sergio Verdú,et al.  Fixed-Length Lossy Compression in the Finite Blocklength Regime , 2011, IEEE Transactions on Information Theory.

[8]  En-Hui Yang,et al.  Universal Multiterminal Source Coding Algorithms With Asymptotically Zero Feedback: Fixed Database Case , 2008, IEEE Transactions on Information Theory.

[9]  Sergio Verdú,et al.  Optimal Lossless Data Compression: Non-Asymptotics and Asymptotics , 2014, IEEE Transactions on Information Theory.

[10]  H. Nagaoka,et al.  Strong converse theorems in the quantum information theory , 1999, 1999 Information Theory and Networking Workshop (Cat. No.99EX371).

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

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

[13]  林 正人 Quantum information : an introduction , 2006 .

[14]  Sidharth Jaggi,et al.  Universal linked multiple access source codes , 2002, Proceedings IEEE International Symposium on Information Theory,.

[15]  S. Sarvotham,et al.  Variable-Rate Universal Slepian-Wolf Coding with Feedback , 2005, Conference Record of the Thirty-Ninth Asilomar Conference onSignals, Systems and Computers, 2005..

[16]  Meir Feder,et al.  On the calculation of the minimax-converse of the channel coding problem , 2015, 2017 IEEE International Symposium on Information Theory (ISIT).

[17]  Massimo Franceschetti,et al.  Random Access: An Information-Theoretic Perspective , 2012, IEEE Transactions on Information Theory.

[18]  Feller William,et al.  An Introduction To Probability Theory And Its Applications , 1950 .

[19]  Tracey Ho,et al.  On the power of cooperation: Can a little help a lot? , 2014, 2014 IEEE International Symposium on Information Theory.

[20]  Emre Telatar,et al.  Variable length coding over an unknown channel , 2006, IEEE Transactions on Information Theory.

[21]  Sergio Verdú,et al.  Variable-Length Compression Allowing Errors , 2014, IEEE Transactions on Information Theory.

[22]  H. Vincent Poor,et al.  Channel Coding Rate in the Finite Blocklength Regime , 2010, IEEE Transactions on Information Theory.

[23]  Michael Langberg,et al.  Network coding: Is zero error always possible? , 2011, 2011 49th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

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

[25]  Michelle Effros,et al.  Random Access Channel Coding in the Finite Blocklength Regime , 2021, IEEE Transactions on Information Theory.

[26]  Sang Joon Kim,et al.  A Mathematical Theory of Communication , 2006 .

[27]  V. Bentkus On the dependence of the Berry–Esseen bound on dimension , 2003 .

[28]  Stark C. Draper Universal Incremental Slepian-Wolf Coding , 2004 .

[29]  Ankur A. Kulkarni,et al.  Improved Finite Blocklength Converses for Slepian–Wolf Coding via Linear Programming , 2018, IEEE Transactions on Information Theory.

[30]  Michelle Effros,et al.  Lossless Source Coding in the Point-to-Point, Multiple Access, and Random Access Scenarios , 2019, 2019 IEEE International Symposium on Information Theory (ISIT).

[31]  Hiroki Koga,et al.  Information-Spectrum Methods in Information Theory , 2002 .

[32]  Adrià Tauste Campo,et al.  Converse bounds for finite-length joint source-channel coding , 2012, 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[33]  Sergio Verdú,et al.  Lossy Joint Source-Channel Coding in the Finite Blocklength Regime , 2012, IEEE Transactions on Information Theory.

[34]  Te Sun Han,et al.  Second-order Slepian-Wolf coding theorems for non-mixed and mixed sources , 2012, 2013 IEEE International Symposium on Information Theory.

[35]  Wei Yu,et al.  Rateless Slepian-Wolf Codes , 2005, Conference Record of the Thirty-Ninth Asilomar Conference onSignals, Systems and Computers, 2005..