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Sandeep Pradhan's
research interests are in the area of - distributed representation and
transmission of information in sensor networks, reliability in
multiuser communication systems such as wireless networks,
multiple description source coding, multiuser information theory and coding

- Structured Codes for network communication
The prevalent trend in information theory has been to obtain performance limits of communication using Shannon's random coding arguments and its multi-terminal extensions, and in coding theory, the goal has been to achieve these limits using algebraic-structured codes with fast encoding and decoding algorithms. It turns out that algebraic structure can also be used to weed out bad codes from an ensemble. In this work, we build on this observation, and look at the distributed source coding problem. We present a new approach to this problem and an achievable rate-distortion region (an inner bound to the performance limit) based on ``good'' random nested codes built over abstract abelian groups. We demonstrate rate gains for this problem over traditional coding schemes using random unstructured codes. For certain sources and distortion functions, the new rate region is strictly bigger than the Berger-Tung rate region, which has been the best known achievable rate region for this problem till now. Further, there is no known unstructured random coding scheme that achieves these rate gains.

Recent talks: [Talk given at AAECC in 2007] [Talk given at ITA in 2009] [Talk given at UC Berkeley in 2009] [Thesis defense of Dinesh Krithivasan in 2010]

- Feedback in Network Communication
Feedback increases the capacity of multi-user channels even when the channels are discrete memoryless. We have developed a novel approach to this problem using a graph-based model for correlated information. The key insight is the following. Before the communication starts, the information of the transmitters are independent. As more channel uses are expended, the posterior correlation (conditioned on the channel output) among the transmitter information increases. This correlation can be exploited to combat interference and channel noise more effectively in subsequent stages.

Recent talks: [Talk given in ISIT 2009]

- Distributed Source Coding
Distributed source coding (compression) deals with separate compression (source coding) of correlated information sources and the goal is to reduce the redundancy in the system. Application areas include sensor networks, digital enhancement of transmissions. The research effort involves the construction of efficient encoder and decoder based on structured channel codes to attain the performance bounds guaranteed by Wyner and Ziv.

Recent talks: [Talk given at CISS in 2000] [Talk given at AAECC in 2007] [Tutorial given at ISIT in 2008]

- Graph-based discrete interface for multiterminal communication
Shannon showed that separate source coding (efficient information representation) and channel coding (efficient information communication) can achieve the optimal end-to-end performance in an information transmission system invloving a transmitter and a receiver. Unfortunately, this separation is suboptimal in a multiuser setting (more than two terminals). In this research effort, we consider a multi-partite graph-based discrete interface for multiuser information transmission problems. Using this approach we can envisage a scenario where many multiterminal sources can be mapped into a graph, and many graphs can be transmissible reliably over a multiterminal channel. In other words, rather than smearing the source coding component and channel coding component into a joint-source-channel-coding block, the proposed approach will enhance them to work with "correlated messages" or graphs, thus retaining the Shannon-style modular approach to multiuser information transmission problems. We are interested in evaluating the performance limits of such interfaces in a Shannon-theoretic framework. In the source coding part, we are interested in the smallest exponent of the size of the graphs that can reliably represent a set of correlated sources, and in the channel coding part, we are interested in the largest exponent of the size of the graphs that can be reliably communicated across a multiuser channel. This has applications in sensor networks.

Recent talks: [Talk given at Washington University in 2007] [Thesis Defense of Suhan Choi in 2006] [Talk given at ITA in 2007]

- Source coding with feedforward
In this problem, the goal is to produce an efficient representation (compression) of a source of information into an index set such that the decoder having access to this can reconstruct the source. The caveat, however, is that the reconstruction has to be realtime and the decoder at the time of reconstructing any source sample has access to the past source samples or past side information samples. The former is called noiseless feedforward, and the latter noisy feedforward. Side information is any extra information that the decoder has access to that is correlated to the original source. This problem can be considered as a functional dual to the problem of channel coding (communication) with feedback. This has applications in sensor networks, economics, control theory and sequential learning theory. In this research effort, we provide information processing strategies and their performance limits. Our goal is also to develop constructive and practical methods for achieving these limits.

Recent talks: [Talk at ITA by Ramji in 2007] [Thesis defense of Ramji Venkataramanan in 2007]

- Multiuser reliability profile
For a multiuser channel (such as a multiple access channel), if the vector of rates of the users is inside the capacity region, then the probability of decoding error of each user can be driven to zero by increasing the block-length. Multiuser reliability profile of a multiuser communication system is the set of rates of decay of probability of errors of the users in the system as a function of block-length. This is very useful especially for wireless communication system. In essence reliability can be thought of as another resource (like power, bandwidth) that can be allocated among the users in a wireless network. Using this concept, we can have heterogeneous quality of service guarantees in such networks directly in the physical layer. The goal of this research effort is to develop efficient strategies for realizing optimum trade-off of reliability among the users in a network.

Recent talks: [Thesis Defense of Lihua Weng in 2005]

- Multiple Description Coding
Multiple description coding deals with efficient encoding of information signals for transmission across packet networks. Applications include video streaming over packet networks. In this research effort we provide new achievable rate regions for multiple descriptions involving more than two channels. We have first formulated the notion of MDS (maximum distance separable) source-channel erasure codes and obtained its rate region information theoretically and then used it in multiple descriptions. This research also covers efficient construction of encoders based on quantizers and trellis-based codes.

Recent talks: [Talk given at DIMACS in 2004]