Successive Coding in Multiuser Information Theory

In this correspondence, we show that solutions to the multiple description coding problem and the broadcast channel coding problem share a common encoding procedure: successive source encoding. We use this connection as the basis for establishing connections between the achievable multiple description rate region and Marton's region for broadcast channels. Specifically, we show that Marton's encoding scheme can be viewed as a multiple description coding procedure. We also explore the dual problem, namely, the relationship between successive channel decoding in multiple access communication and distributed source coding. By illuminating these connections to multiple description, we hope to motivate a solution to what remains a mostly unsolved problem

[1]  Rudolf Ahlswede,et al.  The rate-distortion region for multiple descriptions without excess rate , 1985, IEEE Trans. Inf. Theory.

[2]  Max H. M. Costa,et al.  Writing on dirty paper , 1983, IEEE Trans. Inf. Theory.

[3]  Philip A. Whiting,et al.  Rate-splitting multiple access for discrete memoryless channels , 2001, IEEE Trans. Inf. Theory.

[4]  Jack Edmonds,et al.  Submodular Functions, Matroids, and Certain Polyhedra , 2001, Combinatorial Optimization.

[5]  R. Urbanke,et al.  Asynchronous Slepian-Wolf coding via source-splitting , 1997, Proceedings of IEEE International Symposium on Information Theory.

[6]  Aaron D. Wyner,et al.  Coding Theorems for a Discrete Source With a Fidelity CriterionInstitute of Radio Engineers, International Convention Record, vol. 7, 1959. , 1993 .

[7]  Vivek K. Goyal,et al.  Multiple description coding with many channels , 2003, IEEE Trans. Inf. Theory.

[8]  Abbas El Gamal,et al.  Achievable rates for multiple descriptions , 1982, IEEE Trans. Inf. Theory.

[9]  Toby Berger,et al.  Successive Wyner–Ziv Coding Scheme and Its Application to the Quadratic Gaussian CEO Problem , 2006, IEEE Transactions on Information Theory.

[10]  David Tse,et al.  Multiaccess Fading Channels-Part I: Polymatroid Structure, Optimal Resource Allocation and Throughput Capacities , 1998, IEEE Trans. Inf. Theory.

[11]  Kannan Ramchandran,et al.  n-channel symmetric multiple descriptions - part I: (n, k) source-channel erasure codes , 2004, IEEE Transactions on Information Theory.

[12]  Pramod Viswanath,et al.  Fixed binning schemes: an operational duality between channel and source coding problems with side information , 2004, International Symposium onInformation Theory, 2004. ISIT 2004. Proceedings..

[13]  Shlomo Shamai,et al.  On the achievable throughput of a multiantenna Gaussian broadcast channel , 2003, IEEE Transactions on Information Theory.

[14]  Wei Yu,et al.  Sum capacity of Gaussian vector broadcast channels , 2004, IEEE Transactions on Information Theory.

[15]  Mung Chiang,et al.  Duality between channel capacity and rate distortion with two-sided state information , 2002, IEEE Trans. Inf. Theory.

[16]  David Tse,et al.  Sum capacity of the vector Gaussian broadcast channel and uplink-downlink duality , 2003, IEEE Trans. Inf. Theory.

[17]  Toby Berger,et al.  New results in binary multiple descriptions , 1987, IEEE Trans. Inf. Theory.

[18]  Wei Yu Duality and the Value of Cooperation in Distributive Source and Channel Coding Problems ∗ , .

[19]  Aaron D. Wyner,et al.  The rate-distortion function for source coding with side information at the decoder , 1976, IEEE Trans. Inf. Theory.

[20]  B. Girod,et al.  Illustration of the duality between channel coding and rate distortion with side information , 2000, Conference Record of the Thirty-Fourth Asilomar Conference on Signals, Systems and Computers (Cat. No.00CH37154).

[21]  Bixio Rimoldi Generalized time sharing: A low-complexity capacity-achieving multiple-access technique , 2001, IEEE Trans. Inf. Theory.

[22]  Rudiger Urbanke,et al.  On the structure of the dominant face of multiple-access channels , 1999, Proceedings of the 1999 IEEE Information Theory and Communications Workshop (Cat. No. 99EX253).

[23]  Muriel Médard,et al.  Low-Complexity Approaches to Slepian–Wolf Near-Lossless Distributed Data Compression , 2006, IEEE Transactions on Information Theory.

[24]  Chao Tian,et al.  Multiple Description Quantization Via Gram–Schmidt Orthogonalization , 2005, IEEE Transactions on Information Theory.

[25]  P. Viswanath,et al.  Fixed Binning Schemes for Channel and Source Coding Problems : An Operational Duality , 2003 .

[26]  Rüdiger L. Urbanke,et al.  A rate-splitting approach to the Gaussian multiple-access channel , 1996, IEEE Trans. Inf. Theory.

[27]  Kannan Ramchandran,et al.  Duality between source coding and channel coding and its extension to the side information case , 2003, IEEE Trans. Inf. Theory.

[28]  Toby Berger,et al.  An upper bound on the sum-rate distortion function and its corresponding rate allocation schemes for the CEO problem , 2004, IEEE Journal on Selected Areas in Communications.

[29]  Patrick P. Bergmans,et al.  A simple converse for broadcast channels with additive white Gaussian noise (Corresp.) , 1974, IEEE Trans. Inf. Theory.

[30]  Katalin Marton,et al.  A coding theorem for the discrete memoryless broadcast channel , 1979, IEEE Trans. Inf. Theory.

[31]  Shlomo Shamai,et al.  Nested linear/Lattice codes for structured multiterminal binning , 2002, IEEE Trans. Inf. Theory.

[32]  Gregory W. Wornell,et al.  The duality between information embedding and source coding with side information and some applications , 2003, IEEE Trans. Inf. Theory.

[33]  Andrea J. Goldsmith,et al.  Duality, achievable rates, and sum-rate capacity of Gaussian MIMO broadcast channels , 2003, IEEE Trans. Inf. Theory.

[34]  L. Ozarow,et al.  On a source-coding problem with two channels and three receivers , 1980, The Bell System Technical Journal.