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This work investigates the problem of blind channel equalization in the presence of burst noise. Modeling impulsive noise distribution as generalized-Gaussian, we derive an appropriate criterion for blind equalization. Our method exploits Wijsman’s theorem and develops a maximal-invariant test of equally-likely pulsed signals against impulsive disturbance. We optimize the test and realize an adaptive equalizer, capable of not only mitigating intersymbol interference but also robust to impulsive disturbance. Numerical simulations, obtained on a baseband digital microwave radio system for amplitude-phase shift keying signaling in additive (generalized Gaussian and symmetric-alpha stable) impulsive environment, confirm the admissibility of the proposed equalizer in terms of robustness and steady convergence.

A comprehensive parametric study is performed on planar inverted-F antenna (PIFA) to investigate the effect of antenna parameters on resonant frequency and fractional bandwidth. Empirical formulas for predicting the fractional bandwidth and resonant frequency of PIFA incorporating most of the antenna parameters are numerically obtained. Comparison is provided between the proposed expressions and the existing counterparts. We believe that these results are useful in the design of PIFA.

The paper presents empirical formulas for predicting the resonant frequency of planar inverted-F antenna (PIFA). The physical parameters of PIFA including the dimensions and positions of (top) planar element, shorting plate, feed plate, and ground plane, are studied. The influence of various parameters on antenna characteristics (resonant frequency) has been investigated comprehensively using simulation software HFSS. Comparison with existing empirical expressions are provided. We believe these results are useful in the design of PIFA.

Bayesian filters can be made robust to outliers if the solutions are developed under the assumption of heavy-tailed distributed noise. However, in the absence of outliers, these robust solutions perform worse than the standard Gaussian assumption based filters. In this work, we develop a novel robust filter that adopts both Gaussian and multivariate t-distributions to model the outliers contaminated measurement noise. The effects of these distributions are combined within a Bayesian Model Averaging (BMA) framework. Moreover, to reduce the computational complexity of the proposed algorithm, a restricted variational Bayes (RVB) approach handles the multivariate t-distribution instead of its standard iterative VB (IVB) counterpart. The performance of the proposed filter is compared against a standard cubature Kalman filter (CKF) and a robust CKF (employing IVB method) in a representative simulation example concerning target tracking using range and bearing measurements. In the presence of outliers, the proposed algorithm shows a 38 % improvement over CKF in terms of root-mean-square-error (RMSE) and is computationally 2.5 times more efficient than the robust CKF.

In this work, a variable step-size (VSS) blind equalization algorithm is presented for a multimodulus blind equalization scheme. The parameters of the proposed algorithm are selected using a particle swarm optimization strategy. Eventually, the best parameters for the proposed VSS blind equalization algorithm are obtained and better performance is obtained when compared to the trial-and-error method for choosing these parameters. Ultimately, a considerable reduction in computational complexity when compared to the fixed-step algorithm with extra constraints. Simulation results support this new proposed technique.

Unsupervised equalizers found its use in bandwidth hungry applications (wired, wireless or optical). These equalizer show non-linear behavior in their error functions that are used to update the filter weight coefficients which makes it challenging in order to evaluate their performance in terms of ISI. In this paper, we present the performance analysis of a blind equalizer by varying its taps/length in order to optimize inter symbol interference (ISI) efficiently. Two algorithms are used for blind equalization in a modern wireless communication system i.e. Multimodulus algorithm (MMA) and Square Contour algorithm (SCA) respectively. The channel selected for simulation is frequency selective and time invariant in nature. The performance metrics are average ISI and bit error rate (BER). The overall performance is degraded in terms of average ISI by increasing filter taps using MMA and SCA as a blind equalizer. MMA is more powerful, stable and robust algorithm in terms of low SNR regimes and for increased filter length as compared to SCA respectively. The performance of MMA and SCA algorithms show similar performance behavior initially but for higher filter length, the performance of SCA is going to degrade keeping the same SNR regime.

In this work, we present an adaptive non-data-aided compensator for the in-phase/quadrature-phase (I/Q) mismatch in low-intermediate frequency (heterodyne) receivers. In particular, the adaptive I/Q mismatch algorithm is derived by exploiting the uncorrelatedness between the desired and the image signals, and is used to compensate for gain imbalance and phase-offset error. Furthermore, the frequency-offset error is separately compensated by using an adaptive minimization of a fourth-order statistics based cost function.

Polarization-division multiplexing (PDM) has emerged as a promising technique for increasing data rates without increasing symbol rates. However, the distortion effects of the fiber transmission medium poses severe barriers for the implementation of this technological alternative. Especially, due to the fiber-induced polarization fluctuation orthogonally transmitted PDM signals are mixed at the receiver input. Therefore, a receiver compensation structure needs to be implemented to recover the original orthogonal transmitted components from their mixtures at the end of the fiber channel. This is in fact the focus of this article where a receiver algorithm is based on a recently proposed minimum entropy equalization scheme exploiting the maximization of (an enhanced) energy cost function subject to the magnitude boundedness of (incoming) digital communication signals. Through the use of this scheme, new receiver algorithms for recovering the original polarization signals in an adaptive manner are proposed. The key feature of these algorithms is that they can achieve high equalization performance while maintaining the algorithmic complexity in a fairly low level that is suitable for implementation in optical fiber communication receivers. The performance of these algorithms for a square-QAM based coherent polarization division multiplexed system are illustrated through some simulation examples.

Modulation classification is a signal processing technique which can estimate the modulation format of the received signals using multiple hypotheses test In this paper, we have presented an overview of modulation classification techniques based on Goodness-of-Fit tests. We have discussed the classification performance of modulation classification method based on Anderson Darling (AD) test, Cramer Von Mises(CVM) test and Kohnogorov-Smirnov (K-S) test. The results have been evaluated using Quadrature Amplitude Modulation (QAM) for AWGN channel using Monte Carlo simulations.

Envelop-based (EVB) SNR estimators are robust to carrier offset and phase jitter. The performance of such estimators however, degrades with increasing SNR for nonconstant modulus constellations. In this work, we consider the SNR estimation as an optimum scale-invariant rank discrimination testing problem and propose an EVB algorithm that does not fail at high SNR. The performance of this algorithm is evaluated for 16 and 64 square-QAM and compared with popular EVB algorithms, i.e., M_2M_4 and M_6. Under the assumption of no phase/frequency offset errors, the simulation results show comparable performance at low SNR with existing algorithms and a significant improvement at higher SNR.

A modulation classification method based on modified Kolmogorov-Smirnov (K-S) test is proposed. Unlike modulation classification based on K-S test, the proposed method evaluates the sum of the difference between the empirical distribution obtained from the features extracted from received signals and the hypothesized distribution for each modulation candidate in pool. The results have been evaluated using Quadrature Amplitude Modulation (QAM) for AWGN channel using Monte Carlo simulations. Extensive simulation results demonstrate that the proposed modulation classification method based on modified K-S test offers superior performance as compared to the method based on K-S test.

The area under the Receiver-Operating-Characteristic (ROC) curve of a detector is a simple and suitable figure-of-merit of its detection capability. However the problem of determining the Area-Under-the-Curve (AUC) of an ROC is a non-trivial exercise and hence it has gone usually unnoticed in literature. In this report we analyze the AUC of the recently proposed enhanced energy detector for spectrum sensing. We derive a simple expression for the AUC which can also be applied to determine the AUC of the conventional energy detector as a special case.

Statistical spectrum sensing is a promising method which can reliably detect the primary users while requiring little prior information in cognitive radio networks. In this paper, we present an overview of sensing methods based on Goodness-of-Fit tests. We discuss the performance of Energy Detector (ED) sensing, Anderson Darling (AD) sensing, Cram'er VonMises(CVM) sensing and Order Statistic (OS) sensing and we compare the results using Monte-Carlo simulations. It is shown that OS sensing outperforms ED sensing, CVM sensing and AD sensing. Next it is shown through simulations that the OS test statistic does not provide maximum probability of detection for a desired probability of false alarm and results are provided showing the regions of high probability of detection for desired probability of false alarm.

In this article, we introduce the principle of minimum entropy in the context of blindly equalizing a digital communication channel. We discuss how to use this principle to design an inter-symbol-interference sensitive cost for an exemplary amplitude-phase shift-keying signaling. We also discuss the admissibility of the proposed cost and the stability of the derived adaptive algorithm.

New multimodulus-based adaptive algorithms are being devised for blind phase recovery in QAM communication systems. Analysis of stationary points, sensitivity, steady-state misadjustment, convergence time and tracking ability are being provided. The proposed algorithm is compared with two existing adaptive algorithms and it is shown that the proposed algorithm can provide much better performance in terms of convergence time and steady-state jitter at the cost of complexity.

In this paper, a modified family of blind equalization algorithms is proposed for use in quadrature amplitude modulation (QAM) based digital communication systems. We have called this family: the generalized multimodulus algorithms: MMA(p, q). This family is derived by introducing two degrees of freedom in the conventional MM criterion. We have shown that one of the members of this family, MM(2, 1), (which, to the best of our knowledge, has never been discussed) exhibits the fastest convergence for 16-QAM signal. The dynamic convergence behavior of MM(2, 1) is analyzed and shown to be conforming with simulation results.

This paper presents a new family of algorithms for blind equalization of cross-QAM signals. It is shown that the proposed Generalized Cross-Contour Algorithm (GCrCA) has a tendency to force the equalizer output to lie onto a cross-shaped contour, yielding a successful removal of ISI and recovering the correct orientation of the constellation simultaneously.

A new class of blind adaptive channel equalization technique is presented which is based on reduced-constellation criterion. Under this criterion, the two-dimensional signal space is replaced with four-point constellation to compute blind estimate of the transmitted signal. The proposed algorithm is shown as a better alternative to an existing family of reduced-constellation (or multimodulus) algorithms. The proposed generalized reduced-constellation algorithm (GRCA) is shown to possess good interference cancellation capabilities.

In this paper, some methods are examined for accelerating the convergence time of a recently proposed blind equalization algorithm [Abrar et al. (2004)]. It is shown that, as in conventional equalizers, the adaptive self-or pre-orthogonalization methods can be exploited to enhance the convergence of a blind equalization algorithm. An improvement in convergence time is reported.

This paper presents a framework of stop-and-go algorithms for the blind equalization of QAM data communication systems. Based on the proposed framework, a new square-contour stop-and-go algorithm is presented.

A new method for blind phase estimation is proposed for cross QAM a communication system which uses the estimates of the sixteenth-order statistics of the received signal. Computer simulations reveal that the proposed estimator is capable of giving about the same root mean square error (RMSE) as that of Cartwright's eighth-order estimator while needing even less than half of the number of samples (for cross QAM constellations)

This paper discusses aframework ofstop-and-go methodfor thestochastic gradient based blind equalization algorithms. Based onthe framework, anewstop-and-go square-contour algorithm isproposed. Theproposed algorithm issimutlated inMATLABandreal-time implementation iscarried outon DSPkit TMS320C6711.

In this papec a method of accelerating the speed of convergence of a blind equalization algorithm is examined. It is shown that, as in conventional equalizers, the adaptive self-orthogonalization make them appear attractive in blind equalization of a channel. We applied self-orthogonalization to a newly proposed blind equalization scheme [1], [2], known as softconstraint satisfaction multi-modulus algorithm (SCS-MMA-I). We also proposed a stochastic Newtonlike algorithm for SCS-MMA-I. The latter algorithm, which also computes the Hessian of the blind equalization cost function, resulted in better performance.

Error back propagation (EBP) is the most used training algorithm for feedforwrd artiJicia1 neural networks (FFANNs). Howevei; it is generally believed that it is vely slow if it does converge, especially the network size is not too large compared to the problem at hand. The speed of the learning phase depends both on learning rate (LR) and on the choice of activation functions (AFs). In this papei; a non gradient scheme is proposed to enhance the convergence of EBP algorithm; this scheme is based on the observation that keeping high LR and linear AF during startup enhances the learning capability. But as the network output comes close to the target value, a gradual decrement in LR and increment in the slope of AF ensure a better steady state mapping. The proposed scheme is applied on a blind neural equalizer and it performed better than the standard EBP

A new algorithm is presented for the blind equalization of complex signals. This algorithm combines the benefits of the well-known reduced-constellation algorithm (RCA) and decision-directed algorithm (DDA). The contribution lies in the technique of incorporating the sliced symbols (outcome of the decision device) in the weight adaptation process. Unlike the RCA, where the reduced constellation contains only four symbol points, the proposed algorithms uses all symbols of the signal constellation by compacting them in some statistical manner. The steady-state characteristics of the proposed algorithm are demonstrated by way of simulations and are compared to those of RCA. It is observed that the new algorithm has a better steady-state performance than RCA.

In this work, a new algorithm, based on the minimum-disturbance principle with relaxation, is presented for the blind equalization of complex signals. This algorithm combines the benefits of the well-known reduced constellation algorithm (RCA) and constant modulus algorithm (CMA). The convergence characteristics of the proposed algorithm are demonstrated by way of simulations. In addition, closed form expressions are obtained for the statistical (dispersion) constants used in these algorithms.