On the Separation of Lossy Source-Network Coding and Channel Coding in Wireline Networks

This paper proves the separation between source-network coding and channel coding in networks of noisy, discrete, memoryless channels. We show that the set of achievable distortion matrices in delivering a family of dependent sources across such a network equals the set of achievable distortion matrices for delivering the same sources across a distinct network which is built by replacing each channel by a noiseless, point-to-point bit-pipe of the corresponding capacity. Thus a code that applies source-network coding across links that are made almost lossless through the application of independent channel coding across each link asymptotically achieves the optimal performance across the network as a whole.Comment: 5 pages, to appear in the proceedings of 2010 IEEE International Symposium on Information Theory (ISIT

Multiple Description Coding of Discrete Ergodic Sources

We investigate the problem of Multiple Description (MD) coding of discrete ergodic processes. We introduce the notion of MD stationary coding, and characterize its relationship to the conventional block MD coding. In stationary coding, in addition to the two rate constraints normally considered in the MD problem, we consider another rate constraint which reflects the conditional entropy of the process generated by the third decoder given the reconstructions of the two other decoders. The relationship that we establish between stationary and block MD coding enables us to devise a universal algorithm for MD coding of discrete ergodic sources, based on simulated annealing ideas that were recently proven useful for the standard rate distortion problem.Comment: 6 pages, 3 figures, presented at 2009 Allerton Conference on Communication, Control and Computin

Outage Analysis of Uplink Two-tier Networks

Employing multi-tier networks is among the most promising approaches to address the rapid growth of the data demand in cellular networks. In this paper, we study a two-tier uplink cellular network consisting of femtocells and a macrocell. Femto base stations, and femto and macro users are assumed to be spatially deployed based on independent Poisson point processes. We consider an open access assignment policy, where each macro user based on the ratio between its distances from its nearest femto access point (FAP) and from the macro base station (MBS) is assigned to either of them. By tuning the threshold, this policy allows controlling the coverage areas of FAPs. For a fixed threshold, femtocells coverage areas depend on their distances from the MBS; Those closest to the fringes will have the largest coverage areas. Under this open-access policy, ignoring the additive noise, we derive analytical upper and lower bounds on the outage probabilities of femto users and macro users that are subject to fading and path loss. We also study the effect of the distance from the MBS on the outage probability experienced by the users of a femtocell. In all cases, our simulation results comply with our analytical bounds

Universal Compressed Sensing

In this paper, the problem of developing universal algorithms for compressed sensing of stochastic processes is studied. First, R\'enyi's notion of information dimension (ID) is generalized to analog stationary processes. This provides a measure of complexity for such processes and is connected to the number of measurements required for their accurate recovery. Then a minimum entropy pursuit (MEP) optimization approach is proposed, and it is proven that it can reliably recover any stationary process satisfying some mixing constraints from sufficient number of randomized linear measurements, without having any prior information about the distribution of the process. It is proved that a Lagrangian-type approximation of the MEP optimization problem, referred to as Lagrangian-MEP problem, is identical to a heuristic implementable algorithm proposed by Baron et al. It is shown that for the right choice of parameters the Lagrangian-MEP algorithm, in addition to having the same asymptotic performance as MEP optimization, is also robust to the measurement noise. For memoryless sources with a discrete-continuous mixture distribution, the fundamental limits of the minimum number of required measurements by a non-universal compressed sensing decoder is characterized by Wu et al. For such sources, it is proved that there is no loss in universal coding, and both the MEP and the Lagrangian-MEP asymptotically achieve the optimal performance

A Universal Scheme for Wyner–Ziv Coding of Discrete Sources

We consider the Wyner–Ziv (WZ) problem of lossy compression where the decompressor observes a noisy version of the source, whose statistics are unknown. A new family of WZ coding algorithms is proposed and their universal optimality is proven. Compression consists of sliding-window processing followed by Lempel–Ziv (LZ) compression, while the decompressor is based on a modification of the discrete universal denoiser (DUDE) algorithm to take advantage of side information. The new algorithms not only universally attain the fundamental limits, but also suggest a paradigm for practical WZ coding. The effectiveness of our approach is illustrated with experiments on binary images, and English text using a low complexity algorithm motivated by our class of universally optimal WZ codes