Search

The effect of prior knowledge when linear analog codes are used as joint source-channel codes for sources modeled as multivariate Gaussian processes is analyzed. We use information theoretic tools to evaluate the achievable performance gain obtained by exploiting prior knowledge. In order to assess the validity of linear codes in practical scenarios, where exact source statistics are not known, we study the effect of having partial knowledge of the statistics. We model the mismatch of the statistics as an additive perturbation matrix between the real covariance matrix and the postulated covariance matrix in the recovery process. In this setting, we obtain closed form expressions for a deterministic perturbation matrix and using random matrix theory tools we characterize the performance loss for i.i.d. random matrices. [ABSTRACT FROM AUTHOR]

In this paper, we provide an information-theoretic analysis of a Gaussian multiple-input multiple-output multiple access channel (MIMO MAC) with imperfect channel knowledge at the receiver. In particular, we derive inner and outer bounds for the MIMO MAC rate region when the inputs are Gaussian. We then apply these bounds to a Gaussian interference network with receiver cooperation, in which a central processor with incomplete channel state information must jointly decode all the received signals. Then, in the case where the channel knowledge at the receiver is obtained through training signals, we derive the structure of the optimum training signals for all users under a definite and semidefinite rank constraint. Numerical results show that the bounds we derive can be quite tight, confirming the asymptotic analysis conducted for the finite case. Finally, we also investigate the low-SNR and high-SNR regimes, specifically analyzing the minimum required energy per information bit and the wideband slope region in the first case, and the high-SNR slope in the second. [ABSTRACT FROM AUTHOR]

Compressive covariance estimation has arisen as a class of techniques whose aim is to obtain second-order statistics of stochastic processes from compressive measurements. Recently, these methods have been used in various image processing and communications applications, including denoising, spectrum sensing, and compression. Notice that estimating the covariance matrix from compressive samples leads to ill-posed minimizations with severe performance loss at high compression rates. In this regard, a regularization term is typically aggregated to the cost function to consider prior information about a particular property of the covariance matrix. Hence, this paper proposes an algorithm based on the projected gradient method to recover low-rank or Toeplitz approximations of the covariance matrix from compressive measurements. The proposed algorithm divides the compressive measurements into data subsets projected onto different subspaces and accurately estimates the covariance matrix by solving a single optimization problem assuming that each data subset contains an approximation of the signal statistics. Furthermore, gradient filtering is included at every iteration of the proposed algorithm to minimize the estimation error. The error induced by the proposed splitting approach is analytically derived along with the convergence guarantees of the proposed method. The proposed algorithm estimates the covariance matrix of hyperspectral images from synthetic and real compressive samples. Extensive simulations show that the proposed algorithm can effectively recover the covariance matrix of hyperspectral images from compressive measurements with high compression ratios ($8-15\%$ approx) in noisy scenarios. Moreover, simulations and theoretical results show that the filtering step reduces the recovery error up to twice the number of eigenvectors. Finally, an optical implementation is proposed, and real measurements are used to validate the theoretical findings. [ABSTRACT FROM AUTHOR]

In this paper, the capacity region of the linear deterministic interference channel with noisy channel-output feedback (LD-IC-NF) is fully characterized. The proof of achievability is based on random coding arguments and rate splitting, block-Markov superposition coding, and backward decoding. The proof of the converse reuses some of the existing outer bounds and includes new ones obtained using genie-aided models. Following the insight gained from the analysis of the LD-IC-NF, an achievability region and a converse region for the two-user Gaussian interference channel with noisy channel-output feedback (G-IC-NF) are presented. Finally, the achievability region and the converse region are proven to approximate the capacity region of the G-IC-NF to within 4.4 bits. [ABSTRACT FROM AUTHOR]

In this paper, the benefits of channel-output feedback in the Gaussian interference channel (G-IC) are studied under the effect of additive Gaussian noise. Using a linear deterministic (LD) model, the signal to noise ratios (SNRs) in the feedback links beyond which feedback plays a significant role in terms of increasing the individual rates or the sum-rate are approximated. The relevance of this work lies in the fact that it identifies the feedback SNRs for which in any G-IC, one of the following statements is true: (a) feedback does not enlarge the capacity region; (b) feedback enlarges the capacity region and the sum-rate is greater than the largest sum-rate without feedback; and (c) feedback enlarges the capacity region but no significant improvement is observed in the sum-rate. [ABSTRACT FROM PUBLISHER]