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In this paper, we examine adaptive compression policies, when the sequence of vector-valued measurements to be compressed is noisy and the compressed variables are themselves noisy. The optimization criterion is information gain. In the case of sequential scalar compressions, the unit-norm compression vectors that greedily maximize per-stage information gain are eigenvectors of an a priori error covariance...
In this paper, we investigate threshold effects associated with swapping of signal and noise subspaces in estimating signal parameters from compressed noisy data. The term threshold effect refers to a catastrophic increase in mean-squared error when the signal-to-noise ratio falls below a threshold SNR. In many cases, the threshold effect is caused by a subspace swap event, when the measured data...
This paper considers linear precoding for time-varying multiple-input multiple-output (MIMO) channels. We show that linear minimum mean-squared error (LMMSE) equalization based on the conjugate gradient (CG) method can result in significantly reduced complexity compared with conventional approaches. This reduction is achieved by incorporating a condition number constraint into the precoder optimization...
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