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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.

In this work, we address the target tracking problem for a coordinate-decoupled Markovian jump-mean-acceleration based maneuvering mobility model. A novel low-complexity alternative to the conventional interacting multiple model (IMM) filter is proposed for this class of mobility models. The proposed tracking algorithm utilizes a bank of interacting filters where the interactions are limited to the mixing of the mean estimates, and it exploits a fixed off-line computed Kalman gain matrix for the entire filter bank. Consequently, the proposed filter does not require matrix inversions during on-line operation which significantly reduces its complexity. Simulation results show that the performance of the low-complexity proposed scheme remains comparable to that of the traditional (highly-complex) IMM filter. Furthermore, we derive analytical expressions that iteratively evaluate the transient and steady-state performance of the proposed scheme, and establish the conditions that ensure the stability of the proposed filter. The analytical findings are in close accordance with the simulated results.

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.

A fractional-norm constrained blind adaptive algorithm is presented for sparse channel equalisation. In essence, the algorithm improves on the minimisation of the constant modulus criteria by adding a sparsity-inducing lp-norm penalty (0 < p ≤ 1). The simulation results demonstrate that the proposed equaliser exploits the inherent channel sparsity effectively and exhibits faster convergence compared with its counterparts.

Exploiting the error variance relation, this letter discusses the evaluation of excess mean square error (EMSE) of constant modulus equalizers in a noise-free non-stationary environment. The EMSE analyses are presented for two equalizers - the CMA22 and the βCMA. Simulation results show a close match in theoretical and simulated errors for both algorithms.

In the field of blind source separation, Jacobi-like diagonalization-based approaches constitute an important tool for independent component analysis (ICA). Recently, simultaneous diagonalization of cumulant matrices of third- and fourth-order has been studied by a number of authors. In this work, we present an optimal parametrized composition of these cumulants that puts two classical contrasts, namely, the cumulant-based ICA and the weighted fourth-order contrast in a common framework. It is shown that the optimal weight parameter depends on the a priori statistical knowledge of the original mixing sources. Following the same spirit of the ICA algorithm, we derive the analytical solution for the case of two sources. Finally, a number of computer simulations have been performed to illustrate the behaviour of the Jacobi-like iterations for the maximization of the proposed parametrized contrast.

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.

A new multimodulus algorithm is presented for blind equalization of complex-valued communication channels. The proposed algorithm is obtained by solving a novel deterministic optimization criterion that constituted the dispersion minimization of a priori as well as a posteriori quantities, leading to an update equation having a particular zero-memory continuous nonlinearity. Analyses of automatic phase-recovery and interference cancellation capabilities of the proposed algorithm are provided

Many multi-modulus blind equalization algorithms (MMA) have been presented in the past to overcome the undesirable high misadjustment exhibited by the wellknown constant modulus algorithm. Some of these MMA schemes, specifically tailored for quadrature amplitude modulation (QAM) constellations, have also been proved to fix the phase offset error without needing any rotator at the end of the equalizer stage. In this paper, a new multimodulus algorithm is presented for QAM signals. The contribution lies in the technique to incorporate the sliced symbols (outcomes of decision device) in the multimodulus-based weight adaptation process. The convergence characteristics of the proposed sliced multimodulus algorithm (S-MMA) is demonstrated by way of simulations, and it is shown that it gives better steady-state performance in terms of residual inter-symbol interference and symbol-error rate. It has also been shown that the proposed algorithm exhibits lesser steady-state misadjustment compared to the best reported MMA.

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.

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.