Gregori Vázquez

Robust power allocation algorithms for MIMO OFDM systems with imperfect CSI

This paper presents a Bayesian approach to the design of transmit prefiltering matrices in closed-loop schemes robust to channel estimation errors. The algorithms are derived for a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system. Two different optimization criteria are analyzed: the minimization of the mean square error and the minimization of the bit error rate. In both cases, the transmitter design is based on the singular value decomposition (SVD) of the conditional mean of the channel response, given the channel estimate. The performance of the proposed algorithms is analyzed, and their relationship with existing algorithms is indicated. As with other previously proposed solutions, the minimum bit error rate algorithm converges to the open-loop transmission scheme for very poor CSI estimates.

Conditional maximum likelihood timing recovery: estimators and bounds

This paper is concerned with the derivation of new estimators and performance bounds for the problem of timing estimation of (linearly) digitally modulated signals. The conditional maximum likelihood (CML) method is adopted, in contrast to the classical low-SNR unconditional ML (UML) formulation that is systematically applied in the literature for the derivation of non-data-aided (NDA) timing-error-detectors (TEDs). A new CML TED is derived and proved to be self-noise free, in contrast to the conventional low-SNR-UML TED. In addition, the paper provides a derivation of the conditional Cramer-Rao bound (CRB/sub c/), which is higher (less optimistic) than the modified CRB (MCRB) [which is only reached by decision-directed (DD) methods]. It is shown that the CRB, is a lower bound on the asymptotic statistical accuracy of the set of consistent estimators that are quadratic with respect to the received signal. Although the obtained bound is not general, it applies to most NDA synchronizers proposed in the literature. A closed-form expression of the conditional CRB is obtained, and numerical results confirm that the CML TED attains the new bound for moderate to high E/sub s//N/sub o/.

The Gaussian Assumption in Second-Order Estimation Problems in Digital Communications

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 second-order estimator, which exploits the actual distribution of the discrete constellation. The asymptotic study concludes that the Gaussian assumption leads to the optimal second-order solution if the SNR is very low or if the symbols belong to a multilevel constellation such as quadrature-amplitude modulation (QAM) or amplitude-phase-shift keying (APSK). On the other hand, the Gaussian assumption can yield important losses at high SNR if the transmitted symbols are drawn from a constant modulus constellation such as phase-shift keying (PSK) or continuous-phase modulations (CPM). These conclusions are illustrated for the problem of direction-of-arrival (DOA) estimation of multiple digitally-modulated signals.

Adaptive filter solution for processing lidar returns: optical parameter estimation.

Joint estimation of extinction and backscatter simulated profiles from elastic-backscatter lidar return signals is tackled by means of an extended Kalman filter (EKF). First, we introduced the issue from a theoretical point of view by using both an EKF formulation and an appropriate atmospheric stochastic model; second, it is tested through extensive simulation and under simplified conditions; and, finally, a first real application is discussed. An atmospheric model including both temporal and spatial correlation features is introduced to describe approximate fluctuation statistics in the sought-after atmospheric optical parameters and hence to include a priori information in the algorithm. Provided that reasonable models are given for the filter, inversion errors are shown to depend strongly on the atmospheric condition (i.e., the visibility) and the signal-to-noise ratio along the exploration path in spite of modeling errors in the assumed statistical properties of the atmospheric optical parameters. This is of advantage in the performance of the Kalman filter because they are often the point of most concern in identification problems. In light of the adaptive behavior of the filter and the inversion results, the EKF approach promises a successful alternative to present-day nonmemory algorithms based on exponential-curve fitting or differential equation formulations such as Kletts method.

Second-order parameter estimation

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 found to depend on the observation fourth-order moments that can be calculated analytically from the known signal model. Unfortunately, in most cases, the performance of this estimator is severely limited by the residual bias inherent to nonlinear estimation problems. To overcome this limitation, the second-order minimum variance unbiased estimator is deduced from the general solution by assuming accurate prior information on the vector of parameters. This small-error approximation is adopted to design iterative estimators or trackers. It is shown that the associated variance constitutes the lower bound for the variance of any unbiased estimator based on the sample covariance matrix. The paper formulation is then applied to track the angle-of-arrival (AoA) of multiple digitally-modulated sources by means of a uniform linear array. The optimal second-order tracker is compared with the classical maximum likelihood (ML) blind methods that are shown to be quadratic in the observed data as well. Simulations have confirmed that the discrete nature of the transmitted symbols can be exploited to improve considerably the discrimination of near sources in medium-to-high SNR scenarios.

Parameter estimation of binary CPM signals

Estimation of frequency and symbol timing in continuous phase modulated (CPM) signals is investigated. Several well-known statistical approaches, classically applied to the sensor array problem, are used to derive non-data-aided (NDA) algorithms under a unifying general framework (estimation-directed). A new cost function is proposed which is shown to provide a good compromise between additive and pattern noise cancellation, when the additive noise power is unknown.

Transmit filter optimization based on partial CSI knowledge for wireless applications

This paper deals with closed-loop schemes in OFDM modulation systems. A Bayesian approach is presented to design minimum BER prefiltering matrices in the presence of channel uncertainties. This formulation leads to a robust algorithm that exhibits lower sensitivity to channel estimation errors than classical schemes. The proposed design encompasses the single antenna transmission, the beamforming schemes and the frequency flat fading channels as particular cases.

Optimal power allocation with partial channel knowledge for MIMO multicarrier systems

Closed-loop schemes optimizing power allocation can be applied if channel status information (CSI) is available at both transmitter and receiver. However, CSI is never ideal and algorithms performance degrades rapidly when CSI is poor. In this contribution a MMSE cost function that takes into account channel uncertainty is proposed for MIMO systems with OFDM modulation. The derived algorithm is robust to channel estimation errors. A closed form solution is obtained for joint transmitter and receiver design that optimally allocates the power over all subcarriers and antennas. Numerical results show the new criterion outperforms other solutions that assume ideal CSI knowledge.

Frequency-Domain GLR Detection of a Second-Order Cyclostationary Signal Over Fading Channels

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), this formulation obviates the need of estimating a cumbersome covariance matrix to characterize the likelihood function. We then derive the generalized likelihood ratio test (GLRT) for the detection of a cyclostationary signal in unknown white noise without the need of a assuming a synchronized receiver. This provides a sound theoretical basis for the exploitation of the cyclostationary feature and highlights an explicit link with classical square timing recovery schemes, which appear implicitly in the core of the GLRT. Moreover, to avoid the well-known sensitivity of cyclostationary-based detection schemes to frequency-selective fading channels, a parametric channel model based on a lower bound on the coherence bandwidth is adopted and incorporated into the GLRT. By exploiting the rank-1 structure of small spectral covariance matrices, the obtained detector outperforms the classical spectral correlation magnitude detector.

Asymptotic equivalence between the unconditional maximum likelihood and the square-law nonlinearity symbol timing estimation

This paper provides a systematic approach to the problem of nondata aided symbol-timing estimation for linear modulations. The study is performed under the unconditional maximum likelihood framework where the carrier-frequency error is included as a nuisance parameter in the mathematical derivation. The second-order moments of the received signal are found to be the sufficient statistics for the problem at hand and they allow the provision of a robust performance in the presence of a carrier-frequency error uncertainty. We particularly focus on the exploitation of the cyclostationary property of linear modulations. This enables us to derive simple and closed-form symbol-timing estimators which are found to be based on the well-known square timing recovery method by Oerder and Meyr. Finally, we generalize the OM method to the case of linear modulations with offset formats. In this case, the square-law nonlinearity is found to provide not only the symbol-timing but also the carrier-phase error.