Jaume Riba

A non-line-of-sight mitigation technique based on ML-detection

Geolocation in non line of sight (NLOS) environments is an important issue in wireless communication networks. In several recent publications related with the nature and magnitude of the NLOS phenomenon it is concluded that this is the major source of error in position estimators based on time measurements. This article presents a new approach to ameliorate the effect of the NLOS exploiting the redundant time measurements in scenarios with more than the minimum number of base stations (BS). This redundant data allows to formulate the problem as a test of hypothesis performing a hard decision to discard the BS considered to be in a NLOS scenario. Numerical simulations show that the proposed algorithm can discard the NLOS errors in certain scenarios. The algorithm is also compared with some other existing methods to show the advantage of the new approach.

Robust beamforming for interference rejection in mobile communications

The problem of robust beamformer design in the presence of moving sources is considered. A new technique based on a generalization of the constrained minimum variance beamformer is proposed. The method explicitly takes into account changes in the scenario due to the movement of the desired and interfering sources, requiring only estimation of the desired DOA. Computer simulations show that the resulting performance constitutes a compromise between interference and noise rejection, computational complexity, and sensitivity to source movement.

Average performance analysis of circular and hyperbolic geolocation

A comparative performance analysis of four geolocation methods in terms of their theoretical root mean square positioning errors is provided. Comparison is established in two different ways: strict and average. In the strict type, methods are examined for a particular geometric configuration of base stations (BSs) with respect to mobile position, which determines a given noise profile affecting the respective time-of-arrival (TOA) or time-difference-of-arrival (TDOA) estimates. In the average type, methods are evaluated in terms of the expected covariance matrix of the position error over an ensemble of random geometries, so that comparison is geometry independent. Exact semianalytical equations and associated lower bounds (depending solely on the noise profile) are obtained for the average covariance matrix of the position error in terms of the so-called information matrix specific to each geolocation method. Statistical channel models inferred from field trials are used to define realistic prior probabilities for the random geometries. A final evaluation provides extensive results relating the expected position error to channel model parameters and the number of base stations.

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/.

Novel closed-form ML position estimator for hyperbolic location

Geolocation of mobile terminals has become in the last decades an important issue in mobile networks. In the literature, there have been presented several closed-form position estimators based on time-difference-of-arrival (TDOA) measurements. Only Fangs estimator can be considered optimum in the maximum likelihood (ML) sense. Unfortunately, it can only be applied to the particular case of two TDOA measurements for the two dimensional (2D) location case. This paper presents an extension of this closed-form estimator to be applied to an arbitrary number of TDOA measurements by means of a transformation in the maximum likelihood function. This allows the ML function minimization to be split in several partial ML minimizations which only consider a subset of the available measurements, where the original Fangs estimator can be applied. Numerical simulations show that the proposed algorithm, that can be considered asymptotically the ML-estimator, attains the theoretical limits for all range of reasonable SNR values and has a low implementation complexity.

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.

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.

A Nondata-Aided SNR Estimation Technique for Multilevel Modulations Exploiting Signal Cyclostationarity

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. Exploiting cyclostationarity proves to be advantageous due to the fact that a signal-free Euclidean noise subspace can be identified such that only second order moments of the received waveform need to be computed. The proposed method is nondata-aided (NDA), as well as constellation and phase independent, and only requires prior timing synchronization to fully exploit the cyclostationarity property. The estimator can also be applied to nonconstant modulus constellations without requiring any tuning, which is a feature not found in existing approaches. Implementation aspects and simpler suboptimal solutions are also provided.

Efficient mobile location from time measurements with unknown variances in dynamic scenarios

This work is focused on the study of the maximum likelihood (ML) mobile position estimator when the quality of the available measurements is not a-priori known. Based on a statistical analysis, a polynomial time-evolution model is used to simplify the ML function, finding a closed-form approximation of the ML estimator. Numerical simulations show that the proposed algorithm, with a low implementation complexity, attains the Cramer Rao lower bound (CRB) for all reasonable observed window lengths and for any arbitrary distribution of the measurement variances. Although the mathematical development of this closed-form position estimator is quite dense, the obtained algorithm has a very low complexity implementation.

A novel estimator and performance bound for time propagation and Doppler based radio-location

This paper presents the theoretical accuracy limits of the geolocation algorithms based on TOA measurements exploiting the fact that the mobile is moving in a known or unknown direction. The developed expressions show us the possible improvements in terms of accuracy and/or availability due to the diversity created with the movement. A simple algorithm based on the use of TOA drift estimation is also presented in order to compare its performance with the developed theoretical limits. The proposed estimator attains the theoretical limits under certain conditions.