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In this paper we study the performance of linear analog coding of multivariate Gaussian sources and compare it to the theoretical limits. A general performance analysis is presented for both random and optimal linear encoders. Simulation results show the agreement between the theoretical analysis and the practical implementation.
We study the performance of distributed analog linear coding of multiple correlated multivariate Gaussian sources over the multiple access channel. Assuming the correlation structure is known both at the encoder and the decoder, optimal linear encoding and decoding is introduced. A general performance analysis is presented for both random and optimal linear encoders and compared to a lower bound on...
We study the performance of distributed analog linear coding of correlated multivariate Gaussian sources over the multiple access channel. Assuming the correlation structure is known both at the encoder and the decoder, optimal linear encoding and decoding is introduced. A general performance analysis is presented for both random and optimal linear encoders and compared to a lower bound on the achievable...
Recent work has shown that compressed sensing can be successfully applied in distributed scenarios. In this framework, we study the exploitation of the correlation statistics in the encoding and recovery processes for independently transmitted Gaussian correlated sources, obtaining the optimal projection matrices in the MMSE sense. Encoding is performed separately for each source using either random...
Recent developments in compressed sensing have shown that if a signal has a low Kolmogorov complexity, then it can be reconstructed from a certain number of random projections. We study the distributed coding of correlated Gaussian sources. Both intra- and inter-correlation models are considered in the source models. Decoding schemes in which it is possible to exploit the existing correlation between...
Recent developments in compressed sensing have shown that if a signal can be compressed in some basis, then it can be reconstructed in such basis from a certain number of random projections. Distributed compressed sensing, where several correlated signals are compressed in a distributed manner, has also been proposed in the literature. By allowing additional distortion, successful recovery in distributed...
Recent developments in compressed sensing have shown that if a signal can be compressed in some basis, then it can be reconstructed in such basis from a certain number of random projections. Distributed compressed sensing, where several correlated signals are compressed in a distributed manner, has also been proposed in the literature. By allowing additional distortion, successful recovery in distributed...
It has been recently shown that if a, signal can be compressed in some basis, then it can be reconstructed in such basis from, a certain number of random, projections. By allowing additional distortion, this holds even if the projections are corrupted by noise. We extend this result by showing that it is possible to exploit prior knowledge (e.g., if the signal is a realization of a stochastic process,)...
This paper illustrates that exploiting the source statistics in the recovery process results in significant performance gains, even if the signal is reconstructed in a basis in which it does not admit a sparse representation. Successful recovery will depend on the capability of exploiting all available a priori information in the basis where reconstruction is performed. The proposed framework is similar...
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