Waveform-independent frame-timing acquisition for UWB signals

In this paper, the problem of frame-level symbol timing acquisition for UWB signals is addressed. The main goal is the derivation of a frame-level timing estimator which does not require any prior knowledge of neither the transmitted symbols nor the received template waveform. The independence with respect to the received waveform is of special interest in UWB communication systems, where a fast and accurate estimation of the end-to-end channel response is a challenging and computationally demanding task. The proposed estimator is derived under the unconditional maximum likelihood criterion, and because of the low power of UWB signals, the low-SNR assumption is adopted. As a result, an optimal frame-level timing estimator is derived which outperforms existing acquisition methods in low-SNR scenarios.Peer Reviewe

Adaptive Beamforming for High Bit Rate Services in the FDD Mode of UTRA

Most time-reference beamforming algorithms suffer from severe beampattern distortion effects when applied to high bit rate services in WCDMA, causing serious performance degradation in terms of output BER, especially at high input SINR levels. These shortcomings are essentially caused by the uplink multiplexing of the traffic channel, which is seen by the base station as a powerful interfering source coming from the direction of arrival of the desired user. In this paper, a semi-blind beamforming technique is proposed as a valid solution to overcome this effect. The suggested scheme resorts to a conditional maximum likelihood approach to the underlying estimation problem and is designed to operate in an iterative fashion.Peer ReviewedPostprint (published version

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.Peer Reviewe

The MVGC multivariate Granger causality toolbox: a new approach to Granger-causal inference

Background: Wiener-Granger causality (“G-causality”) is a statistical notion of causality applicable to time series data, whereby cause precedes, and helps predict, effect. It is defined in both time and frequency domains, and allows for the conditioning out of common causal influences. Originally developed in the context of econometric theory, it has since achieved broad application in the neurosciences and beyond. Prediction in the G-causality formalism is based on VAR (Vector AutoRegressive) modelling. New Method: The MVGC Matlab c Toolbox approach to G-causal inference is based on multiple equivalent representations of a VAR model by (i) regression parameters, (ii) the autocovariance sequence and (iii) the cross-power spectral density of the underlying process. It features a variety of algorithms for moving between these representations, enabling selection of the most suitable algorithms with regard to computational efficiency and numerical accuracy. Results: In this paper we explain the theoretical basis, computational strategy and application to empirical G-causal inference of the MVGC Toolbox. We also show via numerical simulations the advantages of our Toolbox over previous methods in terms of computational accuracy and statistical inference. Comparison with Existing Method(s): The standard method of computing G-causality involves estimation of parameters for both a full and a nested (reduced) VAR model. The MVGC approach, by contrast, avoids explicit estimation of the reduced model, thus eliminating a source of estimation error and improving statistical power, and in addition facilitates fast and accurate estimation of the computationally awkward case of conditional G-causality in the frequency domain. Conclusions: The MVGC Toolbox implements a flexible, powerful and efficient approach to G-causal inference. Keywords: Granger causality, vector autoregressive modelling, time series analysi

NDA waveform estimation in the low-SNR regime

This correspondence addresses the problem of nondata-aided waveform estimation for digital communications. Based on the unconditional maximum likelihood criterion, the main contribution of this correspondence is the derivation of a closed-form solution to the waveform estimation problem in the low signal-to-noise ratio regime. The proposed estimation method is based on the second-order statistics of the received signal and a clear link is established between maximum likelihood estimation and correlation matching techniques. Compression with the signal-subspace is also proposed to improve the robustness against the noise and to mitigate the impact of abnormals or outliers.Peer Reviewe

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.Peer ReviewedPostprint (published version

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.Peer Reviewe

Multi-Step Knowledge-Aided Iterative ESPRIT for Direction Finding

In this work, we propose a subspace-based algorithm for DOA estimation which iteratively reduces the disturbance factors of the estimated data covariance matrix and incorporates prior knowledge which is gradually obtained on line. An analysis of the MSE of the reshaped data covariance matrix is carried out along with comparisons between computational complexities of the proposed and existing algorithms. Simulations focusing on closely-spaced sources, where they are uncorrelated and correlated, illustrate the improvements achieved.Comment: 7 figures. arXiv admin note: text overlap with arXiv:1703.1052