Rate-distortion regions for successively structured multiterminal source coding schemes

Multiterminal source coding refers to separate encoding and joint decoding of multiple correlated sources. Joint decoding requires all the messages to be decoded simultaneously which is exponentially more complex than a sequence of single-message decodings. Inspired by previous work on successive coding strategy, which is based on successive decoding structure, we apply the successive Wyner-Ziv coding to different schemes of multiterminal source coding problem. We address the problem from an information theoretic perspective and determine the rate region for three different multiterminal coding schemes: Gaussian CEO problem, 1-helper problem, and 2-terminal source coding problem. We prove that the optimal sum-rate distortion performance for the CEO problem is achievable using the successive coding strategy which is essentially a low complexity approach for obtaining a prescribed distortion. We show that if the sum-rate tends to infinity for a finite number of agents (sensors), the optimal rate allocation strategy assigns equal rates to all agents. The same result is obtained when the number of agents tends to infinity while the sum-rate is finite. Then, we consider 1-helper source coding scheme where one source provides partial side information to the decoder to help the reconstruction of the other source. Our results show that the successive coding strategy is an optimal strategy in this scheme in the sense of achieving the rate-distortion function. For the 2-terminal source coding problem, we develop connections between source encoding and data fusion steps and prove that the whole rate-distortion region is achievable using the successive coding strategy. Comparing the performance of the sequential coding with the performance of the successive coding, we show that there is no sum-rate loss when the side information is not available at the encoder. This result is of special interest in some applications such as video coding where there are processing and storage constraints at the encoder. Based on the successive coding strategy, we provide an achievable rate-distortion region for the m-terminal source coding. We also consider a distributed network, modeled by CEO problem with Gaussian multiple access channel (MAC), where L noisy observations of a memoryless Gaussian source are transmitted through an additive white Gaussian MAC to a decoder. The decoder wishes to reconstruct the main source with an average distortion D at the smallest possible power consumption in the communication link. Our goal is to characterize the power-distortion region achievable by any coding strategy regardless of delay and complexity. We obtain a necessary condition for achievability of all power-distortion tuples ( P 1 , P 2 ,..., P L , D ). Also, analyzing the uncoded transmission scheme provides a sufficient condition for achievability of ( P 1 , P 2 ,..., P L , D ). Then, we consider a symmetric case of the problem where the observations of agents have the same noise level and the transmitting signals are subject to the same average power constraint. We show that in this case the necessary and sufficient conditions coincide and give the optimal power-distortion region. Therefore, in the symmetric case of Gaussian CEO problem uncoded transmission over a Gaussian MAC performs optimally for any finite number of agent

On the Optimality of Secret Key Agreement via Omniscience

For the multiterminal secret key agreement problem under a private source model, it is known that the maximum key rate, i.e., the secrecy capacity, can be achieved through communication for omniscience, but the omniscience strategy can be strictly suboptimal in terms of minimizing the public discussion rate. While a single-letter characterization is not known for the minimum discussion rate needed for achieving the secrecy capacity, we derive single-letter lower and upper bounds that yield some simple conditions for omniscience to be discussion-rate optimal. These conditions turn out to be enough to deduce the optimality of omniscience for a large class of sources including the hypergraphical sources. Through conjectures and examples, we explore other source models to which our methods do not easily extend

Polar Coding for Secret-Key Generation

Practical implementations of secret-key generation are often based on sequential strategies, which handle reliability and secrecy in two successive steps, called reconciliation and privacy amplification. In this paper, we propose an alternative approach based on polar codes that jointly deals with reliability and secrecy. Specifically, we propose secret-key capacity-achieving polar coding schemes for the following models: (i) the degraded binary memoryless source (DBMS) model with rate-unlimited public communication, (ii) the DBMS model with one-way rate-limited public communication, (iii) the 1-to-m broadcast model and (iv) the Markov tree model with uniform marginals. For models (i) and (ii) our coding schemes remain valid for non-degraded sources, although they may not achieve the secret-key capacity. For models (i), (ii) and (iii), our schemes rely on pre-shared secret seed of negligible rate; however, we provide special cases of these models for which no seed is required. Finally, we show an application of our results to secrecy and privacy for biometric systems. We thus provide the first examples of low-complexity secret-key capacity-achieving schemes that are able to handle vector quantization for model (ii), or multiterminal communication for models (iii) and (iv).Comment: 26 pages, 9 figures, accepted to IEEE Transactions on Information Theory; parts of the results were presented at the 2013 IEEE Information Theory Worksho

Distributed Reception in the Presence of Gaussian Interference

abstract: An analysis is presented of a network of distributed receivers encumbered by strong in-band interference. The structure of information present across such receivers and how they might collaborate to recover a signal of interest is studied. Unstructured (random coding) and structured (lattice coding) strategies are studied towards this purpose for a certain adaptable system model. Asymptotic performances of these strategies and algorithms to compute them are developed. A jointly-compressed lattice code with proper configuration performs best of all strategies investigated.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201