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We address the problem of estimating a random field via a wireless sensor network. We use a Multiple Access Channel (MAC) as the basic building block for such a network. For Gaussian sources over Gaussian MACs, we show that Amplify and Forward scheme (AF) performs well in such sensor network scenarios where the battery power is at a premium. We then extend this result to the hierarchical network scenario...
We consider the problem of transmission of correlated discrete alphabet sources over a Gaussian multiple access channel (GMAC). A distributed bit-to-Gaussian mapping is proposed which yields jointly Gaussian codewords. This can guarantee lossless transmission or lossy transmission with given distortions, if possible. The technique can be extended to the system with side information at the encoders...
We consider the transmission of correlated Gaussian sources over orthogonal Gaussian channels. It is shown that the amplify and forward (AF) scheme which simplifies the design of encoders and the decoder, performs close to the optimal scheme even at high SNR. Also, it outperforms a recently proposed scalar quantizer scheme both in performance and complexity. We also study AF when there is side information...
In this paper we address the problem of transmission of correlated sources over a fading multiple access channel (MAC). We provide sufficient conditions for transmission with given distortions. Next these conditions are specialized to a Gaussian MAC (GMAC). Transmission schemes for discrete and Gaussian sources over a fading GMAC are considered. Various power allocation strategies are also compared.
We consider the problem of transmission of several discrete sources over a multiple access channel (MAC) with side information at the sources and the decoder. Source-channel separation does not hold for this channel. Sufficient conditions are provided for transmission of sources with a given distortion. The channel could have continuous alphabets (Gaussian MAC is a special case). Various previous...
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