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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 the 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).
Enabling all-node-repair in minimum storage regenerating codes
J. Li, X.H. Tang, and C. Tian, submitted, Apr. 2016, IEEE Trans. IT; revised Dec. 2016.
Multilevel diversity coding with regeneration
C. Tian and T. Liu, Vol. 62, No. 9, pp. 4833-4847, Sep. 2016.
Layered exact-repair regenerating codes via embedded erasure correction and block designs
C. Tian, B. Sasidharan, V. Aggarwal, P. Vijay Kumar and V. Vaishampayan, IEEE Trans. Inform. Theory, Vol. 61, No. 4, pp. 1933-1947, Apr. 2015.
Characterizing the rate-region of the (4,3,3) exact-repair regenerating codes
C. Tian, IEEE Journal on Secelected Areas in Communications, Vol. 32, No. 5, 967-975, May 2014.
New coding schemes for the symmetric K-description problem
C. Tian and J. Chen, IEEE Trans. Inform. Theory, Vol. 56, No. 10, pp. 5344-5365, Oct. 2010.
An Approximate Approach to Network Information Theory
Network information theory traditionally asks for complete characterizations of network communication problems, partly motivated by the elegent solution on point-to-point channels given by Shannon. However on most cases, such characterizations turn out to be extremely difficult to find. We develop a methodoloty to instead find approximate solutions, which in most cases are much tractable and in fact leads to nontrivial insight into the problem.
An approximation approach to network information theory
S. Avestimehr, S. N. Diggavi, C. Tian, and D. N. S. Tse, Foundations and Trends in Communications and Information Theory, Vol. 12, No. 1-2, pp. 1-183, Sep. 2015.
Optimality and approximate optimality of source-channel separation in networks
C. Tian, J. Chen, S. N. Diggavi and S. Shamai, IEEE Trans. Inform. Theory, Vol 60, No. 2, pp. 904-918, Feb. 2014.
Approximate characterizations for the Gaussian source broadcast distortion region
C. Tian, S. Diggavi and S. Shamai, IEEE Trans. Inform. Theory, Vol. 57, No. 1, pp. 124-136, Jan. 2011.
Asymmetric multilevel diversity coding and asymmetric multiple descriptions
S. Mohajer, C. Tian and S. N. Diggavi, IEEE Trans. Inform. Theory, Vol. 56, No. 9, pp. 4367-4387, Sep. 2010.
Approximating the Gaussian multiple description rate region under symmetric distortion constraints
C. Tian, S. Mohajer and S. N. Diggavi, IEEE Trans. Inform. Theory, Vol. 55, No. 8, pp. 3869-3891, Aug. 2009.
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.
Matched multiuser Gaussian source channel communications via uncoded schemes
C. Tian, J. Chen, S. Diggavi and S. Shamai, IEEE Trans. IT, Vol. 63, No. 7, pp. 4155-4171, Jul. 2017.
Broadcasting correlated vector Gaussians
L. Song, J. Chen and C. Tian, IEEE Trans. Inform. Theory, Vol. 61, No. 5, pp. 2465-2477, May 2015.
Distortion minimization in Gaussian source coding with fading side-information channel
C.T.K. Ng, C. Tian, A. Goldsmith and S. Shamai, IEEE Trans. Inform. Theory, Vol. 58, No. 9, pp. 5725-5739, Sep. 2012.
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.
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.
Distortion minimization in Gaussian source coding with fading side-information channel
C.T.K. Ng, C. Tian, A. Goldsmith and S. Shamai, IEEE Trans. Inform. Theory, Vol. 58, No. 9, pp. 5725-5739, Sep. 2012.
LDPC code design for asynchronous Slepian-Wolf coding
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.
Side-information scalable source coding
C. Tian and S. Diggavi, IEEE Trans. Inform. Theory, Vol. 54, No. 12, pp. 5591-5608, Dec. 2008.
On multistage successive refinement for Wyner-Ziv source coding with degraded side information
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.
Polar codes for multiple descriptions
Q. Shi, L. Song, C. Tian, J. Chen, and S. Dumitrescu, IEEE Trans. Inform. Theory, Vol. 61, No. 1, pp. 107-119, Jan. 2015..
Capacity-achieving polar codes for arbitrarily permuted parallel channels
E. Hof, I. Sason, S. Shamai and C. Tian, IEEE Trans. Inform. Theory, Vol. 59, No. 3, pp. 1505-1516, Mar. 2013.
Latent capacity region: a case study on symmetric broadcast with common messages
C. Tian, IEEE Trans. Inform. Theory, Vol. 57, No. 6, pp. 3273-3285, Jun. 2011.
Capacity region of reversely degraded Gaussian MIMO broadcast channel
J. Chen and C. Tian, IEEE Globecom, p. 1-6, Honolulu Hawaii, Nov. 2009.
A coding algorithm for constant weight vectors: a geometric approach based on dissections
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 of 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.