The Karhunen-Loeve transform (KLT) is a key element of many signal processing tasks, including approximation, compression, and classification. Many recent applications involve distributed signal processing where it is not generally possible to apply the KLT to the signal; rather, the KLT must be approximated in a distributed fashion.

Abstract — We consider a problem in which two encoders observe different components of a memoryless, Gaussian, vectorvalued source. The encoders separately communicate with a decoder, which attempts to reproduce the vector-valued source subject to constraints on the expected squared error of each component. We complete the determination of the rate region for this problem by determining the minimum sum rate needed to meet a pair of target distortions. The proof involves coupling the problem to a quadratic Gaussian “CEO 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—We prove a new outer bound on the rate–distortion region for the multiterminal source-coding problem. This bound subsumes the best outer bound in the literature and improves upon it strictly in some cases. The improved bound enables us to obtain a new, conclusive result for the binary erasure version of the “CEO problem. ” The bound recovers many of the converse results that have been established for special cases of the problem, including the recent one for the Gaussian two-encoder problem. Index Terms—CEO problem, erasure distortion, multiterminal source coding, outer bound, rate region, rate–distortion. Fig. 1. Separate encoding of correlated sources.

Abstract — It is known that next generation mobile comunications systems will most likely employ multi-cell signal processing-often referred to as network MIMO- in order to improve spectral efficiency and fairness. Many publications exist that predict strong achievable rate improvements, but usually neglecting various practical issues connected to network MIMO. In this paper, we analyse the impact of a constrained backhaul infrastructure and imperfect channel knowledge on uplink network MIMO from an information theoretical point of view. Especially the latter aspect leads to the fact that the channel conditions for which network MIMO is reasonably beneficial are strongly constrained. We observe different base station cooperation schemes in scenarios of maximal 3 base stations and 3 terminals, provide simulation results, and discuss the practicability of the discussed schemes and the implications of our results. I.

We prove a new outer bound on the rate-distortion region for the multiterminal source-coding problem. This bound subsumes the best outer bound in the literature and improves upon it strictly in some cases. The improved bound enables us to obtain a new, conclusive result for the binary erasure version of the “CEO problem.” The bound recovers many of the converse results that have been established for special cases of the problem, including the recent one for the Gaussian version of the CEO problem.

Abstract — In a typical sensor network scenario a goal is to monitor a spatio-temporal process through a number of inexpensive sensing nodes, the key parameter being the fidelity at which the process has to be estimated at distant locations. We study such a scenario in which multiple encoders transmit their correlated data at finite rates to a distant, common decoder over a discrete time multiple access channel under various side information assumptions. In particular, we derive an achievable rate region for this communication problem.

Abstract—We consider the uplink of a backhaul-constrained, MIMO coordinated network. That is, a single-frequency network with

Abstract — We consider the uplink of a backhaul-constrained coordinated cellular network. That is, a single-frequency network with N multi-antenna base stations (BSs) that cooperate in order to decode the users ’ data, and that are linked by means of a lossless backhaul with limited capacity. To implement cooperation among receivers, we propose distributed compression: the cooperative BSs, upon receiving their signals, compress them using a distributed Wyner-Ziv code. Then, they send the compressed vectors to the central unit (also a BS), which implements decoding. In this paper, the achievable rate region of such a network is studied (particularized for the 2-user case). We devise an iterative algorithm that solves the weighted sum-rate optimization, and derives the optimum compression codebooks at the BSs. The extension to more than two users is straightforward. I.

In a typical sensor network scenario a goal is to monitor a spatio-temporal process through a number of inexpensive sensing nodes, the key parameter being the fidelity at which the process has to be estimated at distant locations. We study such a scenario in which multiple encoders transmit their correlated data at finite rates to a distant and common decoder. In particular, we derive inner and outer bounds on the rate region for the random field to be estimated with a given mean distortion.