Marion Berbineau

Automatic modulation recognition using wavelet transform and neural networks in wireless systems

Modulation type is one of the most important characteristics used in signal waveform identification. In this paper, an algorithm for automatic digital modulation recognition is proposed. The proposed algorithm is verified using higher-order statistical moments (HOM) of continuous wavelet transform (CWT) as a features set. A multilayer feed-forward neural network trained with resilient backpropagation learning algorithm is proposed as a classifier. The purpose is to discriminate among different M-ary shift keying modulation schemes and the modulation order without any priori signal information. Pre-processing and features subset selection using principal component analysis is used to reduce the network complexity and to improve the classifiers performance. The proposed algorithm is evaluated through confusion matrix and false recognition probability. The proposed classifier is shown to be capable of recognizing the modulation scheme with high accuracy over wide signal-to-noise ratio (SNR) range over both additive white Gaussian noise (AWGN) and different fading channels.

Blind Digital Modulation Identification for Spatially-Correlated MIMO Systems

Modulation type is one of the most important characteristics used in signal waveform identification and classification. Spatial correlation is a crucial factor for practical multiple-input multiple-output (MIMO) systems. This paper addresses the problem of blind digital modulation identification in spatially-correlated MIMO systems. The proposed algorithm is verified using higher order statistical moments and cumulants of the received signal. The purpose is to discriminate among different M-ary shift keying linear modulation schemes without any priori signal information. This study employs several MIMO techniques to identify the modulation with and without channel state information (CSI). The proposed classifier shows a high identification performance in acceptable signal-to-noise ratio (SNR) range.

Land mobile GNSS availability and multipath evaluation tool

Applications of global navigation satellite system (GNSS) in land transportation systems are already extensively deployed and will certainly continue to grow especially in the framework of intelligent transport systems. However, one of the best-known drawbacks of such a system is the lack of satellite visibility in dense urban areas as well as in some specific embedded railway environments. This restricts considerably GNSS use for extended safety related applications. In this paper, a new tool is proposed to predict the availability of a satellite constellation from the point of view of the land transportation user. Knowing the trajectory of a land vehicle, the tool predicts the number of satellites that will be received and produces a safety criterion able to qualify the GNSS localization result. A first version of the tool, already in operation, merges an image processing approach providing the knowledge of the land environment, and the output of a satellite tracking program predicting satellite positions in the sky. This allows us to determine, using a simple optical approach, the number of satellites received in line-of-sight or blocked, with regard to the nearby environment of the receiving antenna. Results obtained in railway as well as in road environments show that satellite signals received by multipath are often used by GNSS receivers in the localization process. Thus, propagation characteristics of the satellite signals in an urban canyon configuration were characterized to determine when a signal received by reflected ray is used by the receiver or not. A criterion related to the satellite elevation is defined to improve the overall performance of the predictive tool. Comparisons with real measurements are commented on. Both simulations and measurements are very similar.

Joint Carrier Frequency Offset and Fast Time-Varying Channel Estimation for MIMO-OFDM Systems

In this paper, a novel pilot-aided iterative algorithm is developed for MIMO-OFDM systems operating in fast time-varying environment. An L-path channel model with known path delays is considered to jointly estimate the multi-path Rayleigh channel complex gains and Carrier Frequency Offset (CFO). Each complex gain time-variation within one OFDM symbol is approximated by a Basis Expansion Model (BEM) representation. An auto-regressive (AR) model is built for the parameters to be estimated. The algorithm performs recursive estimation using Extended Kalman Filtering. Hence, the channel matrix is easily computed and the data symbol is estimated with free inter-sub-carrier-interference (ICI) when the channel matrix is QR-decomposed. It is shown that only one iteration is sufficient to approach the performance of the ideal case for which the knowledge of the channel response and CFO is available.

Radio Wave Propagation in Arched Cross Section Tunnels – Simulations and Measurements

For several years, wireless communication systems have been developed for train to infrastructure communication needs related to railway or mass transit applications. The systems should be able to operate in specific environments, such as tunnels. In this context, specific radio planning tools have to be developed to optimize system deployment. Realistic tunnels geometries are generally of rectangular cross section or arch-shaped. Furthermore, they are mostly curved. In order to calculate electromagnetic wave propagation in such tunnels, specific models have to be developed. Several works have dealt with retransmission of GSM or UMTS. Few theoretical or experimental works have focused on 2.4 GHz or 5.8 GHz bands. In this paper, we propose an approach to model radio wave propagation in these frequency bands in straight arch-shaped tunnels using tessellation in multi-facets. The model is based on a Ray Tracing tool using the image method. The work reported in this paper shows the propagation loss variations according to the shape of tunnels. A parametric study on the facets size to model the cross section is conducted. The influence of tunnel dimensions and signal frequency is examined. Finally, some measurement results in a straight arch-shaped tunnel are presented and analyzed in terms of slow and fast fading.

Blind Modulation Identification for MIMO Systems

Modulation type is one of the most important characteristics used in signal waveform identification and classification. In this paper, an algorithm for blind digital modulation identification for multiple-input multiple-output (MIMO) systems is proposed. The suggested algorithm is verified using higher order statistical moments and cumulants of the received signal. A multi-layer neural network trained with resilient backpropagation learning algorithm is proposed as a classifier. The purpose is to discriminate among different M-ary shift keying linear modulation types and the modulation order without any priori signal information. This study covers different MIMO systems with and without channel state information (CSI). The proposed classifier is evaluated through the probability of identification where we show that our proposed algorithm is capable of identifying the modulation scheme with high accuracy in excellent signal-to-noise ratio (SNR) range.

Multiple-Antenna-Based Blind Spectrum Sensing in the Presence of Impulsive Noise

Cognitive radio (CR) was proposed as a solution to the spectrum scarcity problem. One of the basic functions of any CR is spectrum sensing. Most existing works on spectrum sensing consider the Gaussian noise assumption. In practice, this assumption is not always valid since several existing noise types exhibit non-Gaussian and impulsive behavior. Hence, it is very beneficial to study spectrum sensing in the presence of impulsive noise. In this paper, we propose two new multiple-antenna-based spectrum sensing methods, assuming that the underlying noise follows a symmetric α-stable distribution. This assumption is justified by a distribution fitting of some measurements of the noise acting on the GSM-R antennas onboard trains. The first proposed sensing method is based on the covariation properties of α-stable processes, whereas the second proposed method has the strategy of filtering the corrupted signals before applying a traditional spectrum sensing method. These two methods do not require a priori knowledge about the primary-user signal. Simulation results show that the proposed algorithms provide good spectrum sensing performance in the presence of α-stable distributed impulsive noise.

Automatic modulation recognition using wavelet transform and neural network

Modulation type is one of the most important characteristics used in signal waveform identification. An algorithm for automatic modulation recognition has been developed and presented in this study. The suggested algorithm is verified using higher order statistical moments of wavelet transform as a features set. A multi-layer neural network with resilient backpropagation learning algorithm is proposed as a classifier. The purpose is to discriminate different M-ary shift keying modulation types and modulation order without any priori signal information. Pre-processing and features subset selection using principal component analysis will reduce the network complexity and increase the recognizer performance.

Advantages of Simple MIMO Schemes for Robust or High Data Rate Transmission Systems in Underground Tunnels

Robust, reliable and high data rate transmission systems are key components of underground information systems particularly in the case of driverless ones. In this context, wireless systems must be able to maximize data rate and/or robustness while avoiding increases in transmitting power and/or in transmission bandwidth. MIMO techniques are today well known in the scientific community for indoor applications where the propagation channel experiences multipath effects. The work presented in this paper concerns the evaluation of conventional MIMO techniques such as the Alamouti code, spatial multiplexing, and linear precoding in tunnels environments usually considered as correlated channels. After the choice of the best suited channel model and antenna configuration, the simulation of the physical layer based on Wi-Fi IEEE802.11 a/g standards gives rise to the comparison of the MIMO algorithms.

Radio wave propagation in curved rectangular tunnels at 5.8 GHz for metro applications

The need for wireless communication systems is increasing in the transport domain. These systems have to be operational in every type of environment and particularly tunnels for metro applications. These ones can have rectangular, circular or arch-shaped cross section. Furthermore, they can be straight or curved. This paper presents a new method to model the radio wave propagation in straight tunnels with an arch-shaped cross section and in curved tunnels with a rectangular cross section. The method is based on a Ray Launching technique combining the computation of intersection with curved surfaces, an original optimization of paths, a reception sphere, an IMR technique and a last criterion of paths validity. Results obtained with our method are confronted to results of literature in a straight arch-shaped tunnel. Then, comparisons with measurements at 5.8 GHz are performed in a curved rectangular tunnel.