Random coding arguments are the backbone of most channel capacity achievability proofs. In this paper, we show that in their standard form, such arguments are insufficient for proving some network capacity theorems: structured coding arguments, such as random linear or lattice codes, attain higher rates. Historically, structured codes have been studied as a stepping stone to practical constructions. However, Körner and Marton demonstrated their usefulness for capacity theorems through the derivation of the optimal rate region of a distributed functional source coding problem. Here, we use multicasting over finite field and Gaussian multiple-access networks as canonical examples to demonstrate that even if we want to send bits over a network, structured codes succeed where simple random codes fail. Beyond network coding, we also consider distributed computation over noisy channels and a special relay-type problem. I.

Consider a pair of correlated Gaussian sources (X1, X2). Two separate encoders observe the two components and communicate compressed versions of their observations to a common decoder. The decoder is interested in reconstructing a linear combination of X1 and X2 to within a mean-square distortion of D. We obtain an inner bound to the optimal rate-distortion region for this problem. A portion of this inner bound is achieved by a scheme that reconstructs the linear function directly rather than reconstructing the individual components X1 and X2 first. This results in a better rate region for certain parameter values. Our coding scheme relies on lattice coding techniques in contrast to more prevalent random coding arguments used to demonstrate achievable rate regions in information theory. We then consider the case of linear reconstruction of K sources and provide an inner bound to the optimal rate-distortion region. Some parts of the inner bound are achieved using the following coding structure: lattice vector quantization followed by “correlated” lattice-structured binning.

Abstract not found

Abstract not found

Abstract — In this paper we consider the separate coding problem for L + 1 correlated Gaussian memoryless sources. We deal with the case where L separately encoded data of sources work as side information at the decoder for the reconstruction of the remaining source. The determination problem of the rate distortion region for this system is the so called many-help-one problem and has been known as a highly challenging problem. The author determined the rate distortion region in the case where the L sources working as partial side information are conditionally independent if the remaining source we wish to reconstruct is given. This condition on the correlation is called the CI condition. In this paper we extend the author’s previous result to the case where L + 1 sources satisfy a kind of tree structure on their correlation. We call this tree structure of information sources the TS condition, which contains the CI condition as a special case. In this paper we derive an explicit outer bound of the rate distortion region when information sources satisfy the TS condition. We further derive an explicit sufficient condtion for this outer bound to be tight. In particular, we determine the rate sum part of the rate distortion region for the case where information sources satisfy the TS condition. For some class of Gaussian sources with the TS condition we derive an explicit recursive formula of this rate sum part. Index Terms — Multiterminal source coding, many-help-one problem, Gaussian, rate-distortion region, CEO problem.

Abstract not found