The Gaussian CEO Problem for Scalar Sources With Arbitrary Memory

In this paper, we consider the achievable sum-rate/distortion tradeoff for the Gaussian central estimation officer (CEO) problem with a scalar source having arbitrary memory. We describe how the arbitrary memory problem can be fully characterized by using known results for the vector CEO problem, and then we formulate the variational problem of minimizing the sum-rate subject to a distortion constraint. To solve the problem, we extend the conventional Lagrange method and show that if the solution exists, it should consist of a zero part and a non-zero part, where the non-zero part is determined by solving a set of Euler equations. By calculating the second variation of the min-sum-rate problem, a sufficient condition is also found that can be used to determine if the necessary solution results in the minimal sum rate. The special case of two terminals is examined in detail, and it is shown that an analytical solution is possible in this case. Analysis and discussion with examples are provided to illustrate the theoretical results. The general solution obtained in this paper is shown to be compatible with the previous results for cases such as the problem of rate evaluation for sources without memory.

[1]  Jie Chen,et al.  On the achievable sum rate of multiterminal source coding for a correlated Gaussian vector source , 2012, 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[2]  D. Harville Matrix Algebra From a Statistician's Perspective , 1998 .

[3]  Toby Berger,et al.  Multiterminal source encoding with one distortion criterion , 1989, IEEE Trans. Inf. Theory.

[4]  Zixiang Xiong,et al.  On the Generalized Gaussian CEO Problem , 2012, IEEE Transactions on Information Theory.

[5]  Toby Berger,et al.  Rate distortion theory : a mathematical basis for data compression , 1971 .

[6]  Zhi-Quan Luo,et al.  Optimal rate allocation for the vector Gaussian CEO problem , 2005, 1st IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2005..

[7]  P. Viswanath,et al.  On the Sum-rate of the Vector Gaussian CEO Problem , 2005, Conference Record of the Thirty-Ninth Asilomar Conference onSignals, Systems and Computers, 2005..

[8]  Thomas M. Cover,et al.  Elements of Information Theory (Wiley Series in Telecommunications and Signal Processing) , 2006 .

[9]  Zixiang Xiong,et al.  Asymmetric code design for remote multiterminal source coding , 2004, Data Compression Conference, 2004. Proceedings. DCC 2004.

[10]  Ali H. Sayed,et al.  Linear Estimation (Information and System Sciences Series) , 2000 .

[11]  Yasutada Oohama,et al.  Rate-distortion theory for Gaussian multiterminal source coding systems with several side informations at the decoder , 2005, IEEE Transactions on Information Theory.

[12]  Zhi-Quan Luo,et al.  Multiterminal Source–Channel Communication Over an Orthogonal Multiple-Access Channel , 2007, IEEE Transactions on Information Theory.

[13]  Vinod M. Prabhakaran,et al.  Rate region of the quadratic Gaussian CEO problem , 2004, International Symposium onInformation Theory, 2004. ISIT 2004. Proceedings..

[14]  Feng Jiang,et al.  The Gaussian CEO problem for a scalar source with memory: A necessary condition , 2012, 2012 Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR).

[15]  Gregory J. Pottie,et al.  Fidelity and Resource Sensitive Data Gathering , 2004 .

[16]  Sriram Vishwanath,et al.  Multi-terminal source coding through a relay , 2011, 2011 IEEE International Symposium on Information Theory Proceedings.

[17]  U. Grenander,et al.  Toeplitz Forms And Their Applications , 1958 .

[18]  Toby Berger,et al.  The CEO problem [multiterminal source coding] , 1996, IEEE Trans. Inf. Theory.

[19]  Yasutada Oohama,et al.  Indirect and Direct Gaussian Distributed Source Coding Problems , 2014, IEEE Transactions on Information Theory.

[20]  Kannan Ramchandran,et al.  Generalized coset codes for distributed binning , 2005, IEEE Transactions on Information Theory.

[21]  Hirosuke Yamamoto,et al.  Source Coding Theory for Multiterminal Communication Systems with a Remote Source , 1980 .

[22]  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.

[23]  Jean Pierre Delmas,et al.  Asymptotic eigenvalue distribution of block Toeplitz matrices and application to blind SIMO channel identification , 2001, IEEE Trans. Inf. Theory.

[24]  R. Zamir,et al.  Rematch and forward: Joint source/channel coding for communications , 2008, 2008 IEEE 25th Convention of Electrical and Electronics Engineers in Israel.

[25]  Zixiang Xiong,et al.  On Multiterminal Source Code Design , 2005, IEEE Transactions on Information Theory.

[26]  Robert M. Gray,et al.  Encoding of correlated observations , 1987, IEEE Trans. Inf. Theory.

[27]  Zhen Zhang,et al.  On the CEO problem , 1994, Proceedings of 1994 IEEE International Symposium on Information Theory.