Iyad Dayoub

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.

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.

Performance of multi-relay coded cooperative diversity in asynchronous code-division multiple-access over fading channels

In this study, multi-relay decode-and forward (DAF) cooperative networks employing convolutional coding are studied for asynchronous direct-sequence code-division multiple-access (DS-CDMA) systems over frequency-selective slow fading channels. The authors show that the full benefits of coded cooperative diversity cannot be achieved if no multi-user interference suppression is employed at the cooperative end. The authors consider two scenarios; perfect and imperfect inter-user channels. In that, the bit-error-rate performance of the cooperative system is investigated for an uplink transmission where a decorrelator detector is used at both the relay and base station receivers. Both simulation and analytical results are presented to demonstrate the diversity gains of the convolutionally coded cooperative network.

Modulation Recognition for MIMO Relaying Broadcast Channels with Direct Link

In this letter, we investigate the performance of modulation identification based on pattern recognition approach using the decision tree (J48) classifier, for multiple-input multiple-output (MIMO) relaying broadcast channels with direct link (source-to-destination). The proposed system identifies the modulation type and order among different M-ary shift-keying linear modulations used by broadband technologies such as long term evolution-advanced (LTE-A) and worldwide interoperability for microwave access (WiMAX). The system under study employs features extraction based on higher order statistics (HOS) of the received signal. Based on receiver operating characteristic (ROC) curves, our study shows that J48 classifier is more efficient than the multilayer perceptron (MLP) classifier trained with resilient backpropagation training algorithm (RPROP) where it achieves close to perfect detection rate (over 99%) with reasonable training time in acceptable signal-to-noise ratio (SNR) range. We also show that the performance of the MIMO relaying broadcast network is remarkably better than the traditional MIMO one.

Channel estimation of OFDM system for high data rate communications on mobile environments

One of the main challenges of the 4th Generation wireless systems is to provide high data rates in mobile environments. In these 4G products, WiMax (worldwide interoperability for microwave access) and LTE (Long Term Evolution) will be the platform technologies. The rapid development of the Internet with new services and applications has created new challenges for the development of mobile communications systems. Many wireless systems offer high data rate services (IEEE 802.11n, IEEE 802.15.3 and IEEE 802.16) [1, 4 and 5]. Possible migration of 3G to 4G (LTE) will increase data rates throughput in the range of 100Mbps. These high data rate applications may be difficult to support with the required quality-of-services (QoS) in high mobility environment. For example, the mobile WiMax can be used at mobile speeds of 60 km/h to 120 km/h sufficient for car applications but not for high speed trains. One possible solution is to use a main router for each coach connected with a single antenna to the external broadband network and to distribute the signal inside the coaches thanks to a repeater. An other architecture consider direct transmission to mobile cellular telephone networks using the 3G technology such as the HSDPA (High Speed Downlink Packet Access) and HSUPA (High Speed Uplink Packet Access) allow high-speed data rates, opening the door to a range of mobile usage. All these future and advanced systems are mainly based on orthogonal frequency division multiplexing (OFDM) known to be resistant to various impairments in the mobile channels. In this paper, a transmission system based on OFDM technique and a solution for channel estimation is proposed. The main results will be presented in the case of high mobility context.

Predicted Eigenvalue Threshold Based Spectrum Sensing with Correlated Multiple-Antennas

In this paper, we consider the problem of sensing a primary user in a cognitive radio network by employing multiple-antennas at the secondary user. Among the many spectrum-sensing methods, the predicted eigenvalue threshold (PET) based method is a promising non-parametric blind method that can reliably detect the primary users without any prior information. Also, a simplified PET sensing method, which needs to compare only one eigenvalue to its threshold, is introduced. A performance comparison between the proposed method and other existing methods is provided. Spatial antenna correlation at the secondary user is a crucial factor for practical systems. The effect of the spatial correlation presence on the different sensing methods is investigated.

Non-parametric multiple-antenna blind spectrum sensing by predicted eigenvalue threshold

In this paper, we consider the problem of sensing a primary user in a cognitive radio network by employing multiple antennas at the secondary user. Among the many spectrum-sensing methods, the predicted eigenvalue threshold (PET) based method is a promising non-parametric blind method that can reliably detect the primary users without any prior information. Then, a simplified PET sensing method, which needs to compare only one eigenvalue to its threshold, is introduced. Compared with the original PET sensing algorithm, the simplified algorithm significantly reduces the computational complexity without any loss in performance. A performance comparison between the proposed method and other existing methods is provided.

Adaptation of the Mode Group Diversity Multiplexing Technique for Radio Signal Transmission Over Multimode Fiber

The large bandwidth of multimode fiber (MMF) makes it a very attractive medium for multiservice transmission in building networks at low cost. The mode group diversity multiplexing (MGDM) technique in graded-index multimode fiber (GI-MMF) has been shown to be less expensive with simpler transmitters and receivers, keeping the same information capacity. However, the classic transmission in MGDM complicates the integration of radio-over-fiber (RoF) services. In this paper, we propose a wired and a RoF model dedicated to broadband indoor applications. This model is based on a modified MGDM where we employ a variation of the transmission power technique and a selection algorithm for the optimal transmitter. To evaluate our system, the bit error rate and symbol error rate have been computed.