Javier Villares

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Cyclostationary processes exhibit a form of frequency diversity. Based on that, we show that a digital waveform with symbol period T can be asymptotically represented as a rank-1 frequency-domain vector process which exhibits uncorrelation at different frequencies inside the Nyquist spectral support of 1/T. By resorting to the fast Fourier transform (FFT),(More)
We propose a scheme for Compressed Sensing in the noiseless setting that reconstructs the original signal operating on a binary graph where the samples are obtained sequentially. The proposed scheme has an affordable computational complexity and a large performance enhancement with respect to similar schemes in the literature, thanks to the proposed(More)
This paper deals with the goodness of the Gaussian assumption when designing second-order blind estimation methods in the context of digital communications. The low- and high-signal-to-noise ratio (SNR) asymptotic performance of the maximum likelihood estimator - derived assuming Gaussian transmitted symbols - is compared with the performance of the optimal(More)
This work provides a general framework for the design of second-order blind estimators without adopting any approximation about the observation statistics or the a priori distribution of the parameters. The proposed solution is obtained minimizing the estimator variance subject to some constraints on the estimator bias. The resulting optimal estimator is(More)
Signal-to-noise ratio (SNR) estimators of linear modulation schemes usually operate at one sample per symbol at the matched filter output. In this paper we propose a new method for estimating the SNR in the complex additive white Gaussian noise (AWGN) channel that operates directly on the oversampled cyclostationary signal at the matched filter input.(More)
This paper addresses the problem of blind detection of a wide-sense stationary (WSS) signal over fading channels. We propose a test statistic which is optimal from a correlation-matching perspective that shows invariance with respect to the noise power and the channel gain. In the blind scenario, we derive the quadratic sphericity test (QST) which exploits(More)
In this paper we develop a new, versatile framework for the design of optimal Non-Data-Aided (NDA) parameter estimators based on the exploitation of the received signal sample covariance matrix. The estimator coefficients are optimized in order to yield minimum mean squared error (MSE) estimates of the parameter. Some linear constraints are introduced into(More)