Pascal Chargé

A non-circular sources direction finding method using polynomial rooting

In this paper, we present a new direction finding algorithm for non-circular sources based on the polynomial rooting technique. Due to the non-circularity characteristics of the impinging sources, the proposed method is able to handle more sources than sensors. Using polynomial rooting instead of a searching technique limits the method to linear uniformly spaced arrays. Polynomial rooting, however, reduces computation cost and enhances resolution power significantly. Computer simulations are used to show the performance of the algorithm.

Efficient Subspace-Based Estimator for Localization of Multiple Incoherently Distributed Sources

In this paper, a new subspace-based algorithm for parametric estimation of angular parameters of multiple incoherently distributed sources is proposed. This approach consists of using the subspace principle without any eigendecomposition of the covariance matrix, so that it does not require the knowledge of the effective dimension of the pseudosignal subspace, and therefore the main difficulty of the existing subspace estimators can be avoided. The proposed idea relies on the use of the property of the inverse of the covariance matrix to exploit approximately the orthogonality property between column vectors of the noise-free covariance matrix and the sample pseudonoise subspace. The resulting estimator can be considered as a generalization of the Pisarenkos extended version of Capons estimator from the case of point sources to the case of incoherently distributed sources. Theoretical expressions are derived for the variance and the bias of the proposed estimator due to finite sample effect. Compared with other known methods with comparable complexity, the proposed algorithm exhibits a better estimation performance, especially for close source separation, for large angular spread and for low signal-to-noise ratio.

Transform domain communication systems from a multidimensional perspective, impacts on bit error rate and spectrum efficiency

Transform domain communication systems (TDCSs) is a technology that avoids frequency underutilisation. Its bit error rate (BER) performance has been well studied until now, but it is still unknown what is the maximum spectrum efficiency one can expect. Focusing on the multidimensional property of the system, the authors show the reachable BER and spectral efficiency they can obtain for any TDCS. Through this article, the authors provide guidelines to ensure that the spectral efficiency is maximised, whereas the BER is minimised. Previous contributions using TDCSs are studied with regard to their dimensionality and they show how their BER and spectral efficiency can be improved.

On the ambiguity of COMET-EXIP algorithm for estimating a scattered source

The extended invariance principle (EXIP) has been applied to the structured covariance estimation of a zero mean Gaussian vector; the resulting method was named COMET (covariance matching estimation techniques). This technique has been recently used for estimating separately and efficiently the direction of arrival (DOA) and angular spread of a scattered source. Unfortunately this new technique presents an ambiguity that limits its utilization in practice. We show in this paper the existence and the origin of this ambiguity and we propose a solution to eliminate this problem without introducing bias. Our approach consists first to add a constraint to the original cost function, and then to replace the constrained problem by an unconstrained problem by using the penalty function method.

A direction finding method under sensor gain and phase uncertainties

An original eigenstructure-based method for direction finding in presence of sensor gain and phase uncertainties is presented. The method has been developed in order to estimate the directions of arrival of non-circular radiating sources for any geometrical array configuration. The gain and phase of each sensor are also provided by this technique. We show that this method works even in the case where the number of sources is larger than the number of sensors. The proposed procedure gives very good results as we show through two demonstrative computer simulations.

An extended cyclic music algorithm

Many modulated communication signals exhibit a cyclostationarity (or periodic correlation) property, corresponding to the underlying periodicity arising from carrier frequencies or baud rates. By exploiting cyclostationarity, the signal direction-of-arrival estimation can be significantly improved. In this paper, we propose a new MUSIC-like [1] direction finding algorithm that exploits cyclostationarity. Our method can be considered as an extension of the Cyclic MUSIC algorithm [2].

A robust cooperative spectrum sensing method against faulty nodes in CWSNs

Cognitive wireless sensor networks (CWSNs) become promising infrastructures, which can improve spectrum utilization of traditional wireless sensor networks (WSNs). For cognition in WSNs, spectrum sensing is one of the most crucial function to prevent hazardous interferences with the licensed users and to identify available spectrum for improving the spectrum utilization. In this paper, we propose a robust cooperative spectrum sensing method based on Dempster-Shafer (D-S) theory. Firstly, taking into account the increase of transmitted data with the rise of the number of sensor nodes and the power limitation of nodes, we propose to adapt the D-S theory to the binary hypothesis test at the local sensing sensor node, in order to reduce the amount of control data to be transmitted. Secondly, we consider that some cognitive nodes may not work as expected. Hence, facing this problem of faulty nodes in CWSNs, we propose an evaluation method which considers simultaneously the sensor node reliability and the mutually supportive degree among different sensor nodes to support adapted decision. Simulation results show that the proposed method allows to improve significantly the detection performance compared to other techniques, even in presence of faulty nodes.

Efficient power allocation strategy in multiuser MIMO broadcast channels

In this paper, sum rate optimization of multiuser multiple-input multiple-output broadcast (MU-MIMO) communication systems with perfect channel state information (CSI) at the base station is investigated. Since power allocation is a signomial optimization problem in the presence of multiuser interference (MUI), it is not a convex problem in general. Several optimal solutions proposed in the literature have exponential computational complexity, which is hard to implement for practice. We propose an iterative water-filling algorithm that takes advantage of the classical simple water-filling principle. The proposed algorithm reduces significantly the computational complexity compared with the methods in the literature only with a negligible performance degradation. In addition, the generalized eigenvalue technique for beamforming design is utilized in this paper for minimizing MUI, the number of users and the number of antennas of each user can be arbitrary. Simulations show that the sum rate of the proposed method is close to the sum capacity of the MU-MIMO broadcast channel, especially in low signal-to-noise ratio (SNR) region.

Using the Douglas-Rachford Algorithm to Optimally Reduce the PAPR

Peak-to-Average Power Ratio (PAPR) is a major challenge to overcome when dealing with multi-carrier systems such as OFDM communication systems. We focus here on signals whose spectrums amplitude respects a fix spectrum mask, as the ones used in Transform Domain Communication Systems. When previous literature only focused on the Projection Onto Convex Sets (POCS) algorithm to reduce the PAPR, we propose here to enhance it and to use the Douglas-Rachford algorithm. The convergence time is then drastically lower. Theory concerning the convergence property and experimental results are brought.

Cooperative Spectrum Sensing with Small Sample Size in Cognitive Wireless Sensor Networks

Cooperative spectrum sensing based on Dempster–Shafer (D–S) theory has attracted a large amount of interest in cognitive wireless sensor networks. However, most of them employ energy detection (ED) in local sensing, where the classical Gaussian approximation of ED is accurate only with a large number of data samples. In this paper, aiming at drastically reduce the computational cost and the sensing process duration, we consider that a small sample size is collected at each node of the network. In this configuration, to perform the D–S fusion we introduce new basic probability of assignment functions derived from the statistics of the eigenvalues of the samples covariance matrix. To that end, we introduce a relevant approximation of the Tracy–Widom distribution that allows us to cope with the small sample size. Simulation results show that the proposed method allows to improve significantly the detection performance compared to other techniques, even with small number of samples.