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

Source Broadcasting to the Masses: Separation has a Bounded Loss

This work discusses the source broadcasting problem, i.e. transmitting a source to many receivers via a broadcast channel. The optimal rate-distortion region for this problem is unknown. The separation approach divides the problem into two complementary problems: source successive refinement and broadcast channel transmission. We provide bounds on the loss incorporated by applying time-sharing and separation in source broadcasting. If the broadcast channel is degraded, it turns out that separation-based time-sharing achieves at least a factor of the joint source-channel optimal rate, and this factor has a positive limit even if the number of receivers increases to infinity. For the AWGN broadcast channel a better bound is introduced, implying that all achievable joint source-channel schemes have a rate within one bit of the separation-based achievable rate region for two receivers, or within $\log_2 T$ bits for $T$ receivers

Remote Source Coding under Gaussian Noise : Dueling Roles of Power and Entropy Power

The distributed remote source coding (so-called CEO) problem is studied in the case where the underlying source, not necessarily Gaussian, has finite differential entropy and the observation noise is Gaussian. The main result is a new lower bound for the sum-rate-distortion function under arbitrary distortion measures. When specialized to the case of mean-squared error, it is shown that the bound exactly mirrors a corresponding upper bound, except that the upper bound has the source power (variance) whereas the lower bound has the source entropy power. Bounds exhibiting this pleasing duality of power and entropy power have been well known for direct and centralized source coding since Shannon's work. While the bounds hold generally, their value is most pronounced when interpreted as a function of the number of agents in the CEO problem

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

On Distributed Linear Estimation With Observation Model Uncertainties

We consider distributed estimation of a Gaussian source in a heterogenous bandwidth constrained sensor network, where the source is corrupted by independent multiplicative and additive observation noises, with incomplete statistical knowledge of the multiplicative noise. For multi-bit quantizers, we derive the closed-form mean-square-error (MSE) expression for the linear minimum MSE (LMMSE) estimator at the FC. For both error-free and erroneous communication channels, we propose several rate allocation methods named as longest root to leaf path, greedy and integer relaxation to (i) minimize the MSE given a network bandwidth constraint, and (ii) minimize the required network bandwidth given a target MSE. We also derive the Bayesian Cramer-Rao lower bound (CRLB) and compare the MSE performance of our proposed methods against the CRLB. Our results corroborate that, for low power multiplicative observation noises and adequate network bandwidth, the gaps between the MSE of our proposed methods and the CRLB are negligible, while the performance of other methods like individual rate allocation and uniform is not satisfactory