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Distributed Data Storage
In this information age, users and digital devices are constantly producing data, and the need for reliable data storage for all these data in the cloud environment has grown exponentially. Next generation of data storage systems have to be reliable, distributed and agile, with a high-availability guarantee. We are working on various aspects of this problem, which includes, e.g., new erasure codes with reduced repair bandwidth requirement, system designs for distributed data storage, distributed data storage for lossy compression (theoretically known as the multiple description problem).
Characterizing the rate-region of the (4,3,3) exact-repair regenerating codes
C. Tian, accepted, JSAC on Communication Methodologies for the Next-Generation Storage Systems
Exact-repair regenerating codes via layered erasure correction and block designs
C. Tian, V. Aggarwal and V. Vaishampayan, submitted, 2013, IEEE Trans. Inform. Theory
C. Tian and J. Chen, IEEE Trans. Inform. Theory, Vol. 56, No. 10, pp. 5344-5365, Oct. 2010.
C. Tian, S. Mohajer and S. N. Diggavi, IEEE Trans. Inform. Theory, Vol. 55, No. 8, pp. 3869-3891, Aug. 2009.
S. Mohajer, C. Tian and S. N. Diggavi, IEEE Trans. Inform. Theory, Vol. 56, No. 9, pp. 4367-4387, Sep. 2010.
J. Chen and C. Tian, IEEE Trans. Inform. Theory, Vol. 52, No. 12, pp. 5197-5217, Dec. 2006.
Joint Source-Channel Coding
The current communication network is built with the well known layered structure, however, underlying this structure is the assumption of such separations do not cause much loss of performance. One fundamental question is whether simplest separation of coding components, i.e., source-channel separation, is optimal or not in complex communication networks, and if not, what is optimal then? We provide some answers to these questions, approximately for some cases, and precisely for some others.
C. Tian, J. Chen, S. N. Diggavi and S. Shamai, accepted, IEEE Trans. Inform. Theory
The achievable distortion region of sending a bivariate Gaussian source on the Gaussian broadcast channel
C. Tian, S. Diggavi and S. Shamai, IEEE Trans. Inform. Theory, Vol. 57, No. 10, pp. 6419-6427, Oct. 2011.
C. Tian, S. Diggavi and S. Shamai, IEEE Trans. Inform. Theory, Vol. 57, No. 1, pp. 124-136, Jan. 2011.
Successive refinement via broadcast: optimizing expected distortion of a Gaussian source over a Gaussian fading channel
C. Tian, A. Steiner, S. Shamai and S. N. Diggavi, IEEE Trans. Inform. Theory, Vol. 54, No. 7, pp. 2903-2918, Jul. 2008..
Source Coding with Side Information
In many communication and data compression systems, various additional information may be available to the encoder or the decoders. Side information is a general term to model such information. The dependence structure between the source and the side information is not fixed, and more complex coding strategy has to be used.
C.T.K. Ng, C. Tian, A. Goldsmith and S. Shamai, IEEE Trans. Inform. Theory, Vol. 58, No. 9, pp. 5725-5739, Sep. 2012.
Z. Sun, C. Tian, J. Chen, and Kon Max Wong, IEEE Trans. Communications, Vol. 58, No. 2, pp. 511-520, Feb. 2010.
Remote vector Gaussian source coding with decoder side information under mutual information and distortion constraints
C. Tian and J. Chen, IEEE Trans. Inform. Theory, Vol. 55, No. 10, pp. 4676-4680, Oct. 2009.
C. Tian and S. Diggavi, IEEE Trans. Inform. Theory, Vol. 54, No. 12, pp. 5591-5608, Dec. 2008.
C. Tian and S. Diggavi, IEEE Trans. Inform. Theory, Vol. 53, No. 8, pp. 2946-2960, Aug. 2007.
Channel Capacity and Code Designs
Channel capacity characterization and code designs are two central themes in information and communication theory. For broadcast channels, the capacity region is unknown, and the two papers below include some progress toward solving this difficult problem. Constant weight code is a classical problem for which we developed a novel algorithm; polar code, on the other hand, is a new development in the field, based on which we developed optimal codes for communication on the parallel channel.
E. Hof, I. Sason, S. Shamai and C. Tian, IEEE Trans. Inform. Theory, Vol. 59, No. 3, pp. 1505-1516, Mar. 2013.
C. Tian, IEEE Trans. Inform. Theory, Vol. 57, No. 6, pp. 3273-3285, Jun. 2011.
J. Chen and C. Tian, IEEE Globecom, p. 1-6, Honolulu Hawaii, Nov. 2009.
C. Tian, V. Vaishampayan and N.J.A. Sloane, IEEE Trans. Inform. Theory, Vol. 55, No. 3, pp. 1051-1060, Mar. 2009.
Coding and Processing for Visual Information
Images as a special type of signals have its own characteristics, and require many special techniques in coding and processing. For example, in multiple description image coding, information theoretic optimal solutions may not suit the best for images. In 3D image processing and object reconstruction, tools such as bilateral filtering turn out to be quite effective, and optimization techniques can be more effectively used by incorporating visual clues. Even for the classical image compression problem, simpler codec proves to be useful: the TCE embedded image codec (ICASSP 04) (available in QccPack software library) has been used by many researchers.