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This correspondence considers communication over two Gaussian channels with additive state interference anti noiseless output feedback-the multiple access channel (MAC) and the augmented broad- cast channel (ABC) (a special class of the physically degraded broadcast channel). The state interference is assumed to be available noncausally at the transmitters. Deterministic coding schemes for these two channels were suggested by Ozarow when there is no state present. Recently, Merhav and Weissman derived an optimal feedback coding scheme for the single-user Gaussian channel with additive state interference. Combining ideas from these two schemes, we extend Ozarow's coding algorithms to the state-dependent case, showing that the capacity regions of the MAC and the ABC are not affected, even though not all parts of the system have knowledge of the state. The proposed schemes attain a better error performance and reduce the coding complexity with respect to the standard random binning coding technique. [ABSTRACT FROM AUTHOR]

Recently, Gallager's bounding techniques have been used to derive tight performance bounds for coded systems in fading channels. Most works in this field have thus far dealt with coherent decoding. This paper develops Gallager bounds for noncoherent systems in fading channels. Unlike coherent decoding, the exact error probability of a noncoherent decoder/detector conditioned on the fading coefficients does not admit a closed-form expression. This difficulty is overcome in this paper by employing the Chernoff technique. Although it weakens the bounds to some extent, the Chernoff technique enables the derivations of the limit-before-average (LBA) bound and Gallager bounds in closed form for noncoherent fading channels. Numerical examples show that the proposed bounds are convergent and are tighter than the conventional union bound. [ABSTRACT FROM AUTHOR]

In this correspondence, we consider the problem of. maximizing sum rate of a multiple-antenna Gaussian broadcast channel (BC). It was recently found that dirty-paper coding is capacity achieving for this channel. In order to achieve capacity, the optimal transmission policy (i.e., the optimal transmit covariance structure) given the channel conditions and power constraint must be found. However, obtaining the optimal transmission policy when employing dirty-paper coding is a computationally complex nonconvex problem. We use duality to transform this problem into a well-structured convex multiple-access channel (MAC) problem. We exploit the structure of this problem and derive simple and fast iterative algorithms that provide the optimum transmission policies for the MAC, which can easily be mapped to the optimal BC policies. [ABSTRACT FROM AUTHOR]

In this paper, we consider a graph-based framework for transmission of correlated sources over multiple-access channels. It is well known that the separation approach is not optimal for this multiuser communication. Our objective in this work is to reintroduce modularity in this problem using a graph-based discrete interface and to minimize the performance loss as compared to the optimal joint source-channel coding scheme. The proposed framework envisages a transmission systems with two modules: a source-coding module and a channel-coding module. In the former module, the correlated sources are encoded distributively into correlated messages whose correlation structure can be associated with a bipartite graph. These correlated messages are then encoded by using correlated codewords and are reliably transmitted over the multiple-access channel -in the latter module. This leads to performance gains in terms of enlarging the class of correlated sources that can be reliably transmitted over a multiple-access channel as compared to the conventional separation approach. We provide an information-theoretic characterization of 1) the rate of exponential growth (as a function of the number of channel uses) of the size of the bipartite graphs whose edges can be reliably transmitted over a multiuser channel and 2) the rate of exponential growth (as a function of the number of source samples) of the size of the bipartite graphs which can reliably represent a pair of correlated sources to be transmitted over a multiuser channel. [ABSTRACT FROM AUTHOR]