49 research outputs found

    Capacity Region of the Broadcast Channel with Two Deterministic Channel State Components

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    This paper establishes the capacity region of a class of broadcast channels with random state in which each channel component is selected from two possible functions and each receiver knows its state sequence. This channel model does not fit into any class of broadcast channels for which the capacity region was previously known and is useful in studying wireless communication channels when the fading state is known only at the receivers. The capacity region is shown to coincide with the UV outer bound and is achieved via Marton coding.Comment: 5 pages, 3 figures. Submitted to ISIT 201

    A New Capacity Result for the Z-Gaussian Cognitive Interference Channel

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    This work proposes a novel outer bound for the Gaussian cognitive interference channel in strong interference at the primary receiver based on the capacity of a multi-antenna broadcast channel with degraded message set. It then shows that for the Z-channel, i.e., when the secondary receiver experiences no interference and the primary receiver experiences strong interference, the proposed outer bound not only is the tightest among known bounds but is actually achievable for sufficiently strong interference. The latter is a novel capacity result that from numerical evaluations appears to be generalizable to a larger (i.e., non-Z) class of Gaussian channels

    Deterministic Capacity of MIMO Relay Networks

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    The deterministic capacity of a relay network is the capacity of a network when relays are restricted to transmitting \emph{reliable} information, that is, (asymptotically) deterministic function of the source message. In this paper it is shown that the deterministic capacity of a number of MIMO relay networks can be found in the low power regime where \SNR\to0. This is accomplished through deriving single letter upper bounds and finding the limit of these as \SNR\to0. The advantage of this technique is that it overcomes the difficulty of finding optimum distributions for mutual information.Comment: Submitted to IEEE Transactions on Information Theor
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