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One such argument is given by Kramer and Gastpar (2006), and another by Gastpar and Kramer (2006b). 6, the dependence-balance bound can be shown to coincide with the max-min cut-set bound. However, for M users, it can be shown that the cut-set bound and the dependence-balance bound lead to different scaling behaviors, and that the cut-set bound is loose. 3. DIGITAL ARCHITECTURES 29 phenomenon), or that the transmitting terminals have some form of cooperation available. The case of dependent messages has been studied by Cover et al.

WM [n])T is a column vector of circularly symmetric complex Gaussian random variables of mean zero and covariance matrix σW2 IM . The encoding task can then be described as follows: Sensor m makes N consecutive observations {um [n]}N n=1 . In some versions of the problem, sensor m may also receive a limited amount of additional information from other sensors, and we will denote this additional information collectively by Vm . Based on the observed sequence {um [n]}N n=1 and the additional information Vm , sensor m decides on a codeword {xm [n]}KN n=1 to be transmitted across the channel.

1995). In this section, we illustrate that in general networks, there is a strict performance penalty for digital communication architectures. 3, we expressed the best possible performance of a digital architecture by equating the rate (region) for the source coding to the capacity (region) of the channel. 83) where R is the rate region required for the source coding step (to satisfy distortion constraints D), and C is the capacity region of the channel network (for given resource constraints P ).

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Das Mathematica-Buch by Wolfram S.

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