Mobien Shoaib

Multichannel Fast QR-Decomposition Algorithms: Weight Extraction Method and Its Applications

Multichannel fast QR decomposition RLS (MC-FQRD-RLS) algorithms are well known for their good numerical properties and low computational complexity. The main limitation is that they lack an explicit weight vector term, limiting themselves to problems seeking an estimate of the output error signal. This paper presents techniques which allow us to use MC-FQRD-RLS algorithms with applications that previously have required explicit knowledge of the adaptive filter weights. We first consider a multichannel system identification setup and present how to obtain, at any time, the filter weights associated with the MC-FQRD-RLS algorithm. Thereafter, we turn to problems where the filter weights are periodically updated using training data, and then used for fixed filtering of a useful data sequence, e.g., burst-trained equalizers. Finally, we consider a particular control structure, indirect learning, where a copy of the coefficient vector is filtering a different input sequence than that of the adaptive filter. Simulations are carried out for Volterra system identification, decision feedback equalization, and adaptive predistortion of high-power amplifiers. The results verify our claims that the proposed techniques achieve the same performance as the inverse QRD-RLS algorithm at a much lower computational cost.

Classification of digitally modulated signals in presence of non-Gaussian HF noise

Automatic Modulation Classification (AMC) is the process of classifying the received signals without prior information. This process is an intermediate step between signal detection and demodulations. It serves both military and civilian applications, such as spectrum monitoring and general-purpose universal demodulators. In this paper, we propose a Decision Tree (DT) algorithm to classify a wide class of the single carrier modulations used in High Frequency (HF) band. Specifically, the proposed algorithm addresses the classification of 2FSK, 4FSK, 8FSK, 2PSK, 4PSK, 8PSK, 16QAM, 32QAM, 64QAM, and OQPSK using three features: Temporal Time Domain (TTD), spectral peaks, and number of amplitude levels. Almost all previous research work in AMC assumes the noise model to be Additive White Gaussian Noise (AWGN). Although this assumption is valid in many communications environments, recent literatures show that the HF noise is fluctuating between AWG and Bi-kappa distributions. This work, first, considers the effect of noise model on the previously mentioned features, and then presents simulation results showing the performance of proposed algorithm in such an environment.

Robustness of digitally modulated signal features against variation in HF noise model

High frequency (HF) band has both military and civilian uses. It can be used either as a primary or backup communication link. Automatic modulation classification (AMC) is of an utmost importance in this band for the purpose of communications monitoring; e.g., signal intelligence and spectrum management. A widely used method for AMC is based on pattern recognition (PR). Such a method has two main steps: feature extraction and classification. The first step is generally performed in the presence of channel noise. Recent studies show that HF noise could be modeled by Gaussian or bi-kappa distributions, depending on day-time. Therefore, it is anticipated that change in noise model will have impact on features extraction stage. In this article, we investigate the robustness of well known digitally modulated signal features against variation in HF noise. Specifically, we consider temporal time domain (TTD) features, higher order cumulants (HOC), and wavelet based features. In addition, we propose new features extracted from the constellation diagram and evaluate their robustness against the change in noise model. This study is targeting 2PSK, 4PSK, 8PSK, 16QAM, 32QAM, and 64QAM modulations, as they are commonly used in HF communications.

Equivalent output-filtering using fast QRD-RLS algorithm for burst-type training applications

Fast QR decomposition RLS (FQRD-RLS) algorithms are well known for their good numerical properties and low computational complexity. The FQRD-RLS algorithms do not provide access to the filter weights, and their uses have so far been limited to problems seeking an estimate of the output error signal. In this paper we present techniques which allow us to reproduce the equivalent output signal corresponding to any input-signal applied to the weight vector of the FQRD-RLS algorithm. As a consequence, we can extend the range of applications of the FQRD-RLS to include problems where the filter weights are periodically updated using training data, and then used for fixed filtering of a useful data sequence, e.g., burst-trained equalizers. The proposed output-filtering techniques are tested in an equalizer setup. The results verify our claims that the proposed techniques achieve the same performance as the inverse QRD-RLS algorithm at a much lower computational cost

Solution to the Weight Extraction Problem in Fast QR-Decomposition RLS Algorithms

Fast QR decomposition RLS (FQRD-RLS) algorithms are well known for their good numerical properties and low computational complexity. However the FQRD-RLS algorithms do not provide access to the filter weights, and so far their use has been limited to problems seeking an estimate of the output error signal. In this paper we present a novel technique to obtain the filter weights of the FQRD-RLS algorithm at any time instant. As a consequence, we extend the range of applications to include problems where explicit knowledge of the filter weights is required. The proposed weight extraction technique is tested in a system identification setup. The results verify our claim that the extracted coefficients of the FQRD-RLS algorithm are identical to those obtained by any RLS algorithm such as the inverse QRD-RLS algorithm

Enhanced Blind Equalization for Optical DP-QAM in Finite Precision Hardware

In this letter, we apply the inverse QR decomposition-constant modulus algorithm (IQRD-CMA), blind equalization technique, for impaired 14 GBd DP-16 quadratic-amplitude modulation optical modulated signal. The IQRD-CMA is considered here to mitigate the effect of residual chromatic dispersion (CD) and polarization mode dispersion (PMD). We evaluate the performance of the proposed inverse QR decomposition in terms of convergence rate, steady-state/residual mean square error (MSE), and bit error rate (BER), in comparison with standard blind constant modulus algorithm (CMA) and recursive least squares (RLS-CMA). Assuming infinite precision, the IQRD-CMA and RLS-CMA achieve similar performance and induce the same computational complexity; however, both largely outperform standard CMA for wide ranges of CD and PMD. However, for finite precision hardware, compared with RLS-CMA, the proposed blind IQRD-CMA clearly achieves one order of BER magnitude at 8- and 10-b resolutions, and further reduces the steady-state MSE by 3 dB. Moreover, results show that IQRD-CMA maintains the system stability at acceptable number of bits resolutions. In addition, this letter addresses the tradeoff between the complexity and the performance of all these equalizers for several precision settings.

Support vector machine based classifier for digital modulations in presence of HF noise

Support vector machines (SVMs) deal with challenging classification problems. One such a challenge is to classify the modulation type of a signal transmitted over High frequency (HF) band. The noise distribution in this band has time varying nature. SVM can mitigate these variations with correct choice of features and kernel functions. This paper presents a feature-based classification method utilizing SVM for the classification of 10 types of modulations in the presence of Gaussian as well as non-Gaussian noise disturbances. The proposed method is able to classify type and order of modulation at relatively low signal-to-noise ratios (SNRs) for both simulated as well as actual data.

A novel approach for automatic classification of digitally modulated signals in HF communications

In this paper, we propose a novel algorithm for automatic modulation classification of single carrier digital modulations widely used in High Frequency (HF) band that serves both military and civilian applications. Specifically, the proposed algorithm addresses the classification of 2FSK, 4FSK, 8FSK, 2PSK, 4PSK, 8PSK, 16QAM, 32QAM, 64QAM, and OQPSK using two features: Dissimilarity in constellation diagram for classification of PSK/QAM signals and spectral peaks for classification of FSK signals with the use of both Decision Tree (DT) and Support Vector Machine (SVM) classifiers. The robustness of proposed algorithm is tested against noise power and changes in noise model. Additive White Gaussian Noise (AWGN) and Bi-kappa noise models are considered in this work, as both exist in HF band.

Reduced Complexity Solution for Weight Extraction in QRD-LSL Algorithms

QR-decomposition-based least-squares lattice (QRD-LSL) algorithms do not provide the transversal weight vector in explicit form. These weights can be computed from the variables of the QRD-LSL algorithm using the Levinson-Durbin (LD) recursion. If the prediction coefficients do not vary over time, a reduced complexity but approximate solution can be obtained. Nonetheless, this approximate solution requires algorithm convergence and infinite memory support (forgetting factor equal to one). To obtain the exact weights at any time instant and for any choice of the forgetting factor, the computational complexity of the true LD recursion increases by an order of magnitude. In this letter, we show that an exact solution can be obtained with a reduced computational complexity and without any added restriction. Simulation results show that the solutions obtained using the proposed method and the exact LD recursion are the same up to the precision used, whereas the weights from the approximate method always deviate from the true solution.

Performance enhancement of cognitive radio Wireless Mesh Networks by link scheduling and beamforming

Cognitive Radio is an emerging technology that allows an efficient spectrum utilization by enabling the temporary use of the unused time-frequency resources in a licensed spectrum without interfering with the transmission of other users. As the cognitive radio is envisaged to solve spectrum scarcity, it can be applied to WMNs resulting in huge gains in terms of network capacity and enhanced quality services for the end user. In this paper, we have presented an efficient beamforming technique, which adds a third search dimension to sensing technology, i.e. the angle, thereby increasing the probability of the unused timefrequency-angle resources. Secondly, we have also proposed an efficient cross layer link scheduling approach to minimize interference, which is a critical factor affecting WMN performance. Simulation results show effectiveness of our proposed methods as the results are closer to the near optimum solution.