In this paper, we derive closed-form exact and asymptotic expressions for the symbol error rate (SER) as well as channel capacity when communicating over the Fox's H-function fading channel. The SER expressions are obtained for numerous practically-employed modulation schemes in case of single as well as three multiplebranch diversity receivers: maximal ratio combining (MRC), equal gain combining (EGC), and selection combining (SC). The derived exact expressions are given in terms of the univariate and multivariate Fox-H functions for which we provide a portable and efficient Python code. Since the Fox's H-function fading channel represents the most generalized fading model ever presented in the literature, the derived expressions subsume most of those previously presented for all the known simple and composite fading models. Moreover, easy-to-compute asymptotic expansions are provided so as to easily study the behavior of the SER and channel capacity at high values of the average signal-to-noise (SNR). The asymptotic expansions are also useful in comparing different modulation schemes and receiver diversity combiners. Numerical and simulation results are also provided to support the mathematical analysis and prove the validity of the obtained expressions.
In this paper, we revisit energy detection-based spectrum sensing cognitive radio systems operating over generalised fading channels. In particular, we derive closed-form exact expressions as well as lowand high-signal-to-noise ratio asymptotic expansions for the misdetection probability over the Fox's H-function fading channel. The closed-form expression is given in terms of the bivariate Fox's Hfunction and subsumes most of the expressions previously presented in the literature. Also, the obtained asymptotic expressions are very easy to compute and can be used to get various performance insights.We verified, theoretically and numerically, the validity of the exact expression for important special cases previously reported in the literature, namely the Nakagami-m and the extended generalised-K (EGK) fading distributions. Numerical results also demonstrate the high accuracy of the asymptotic expansions.
Reducing the dimensionality ofthe training and testing data is crucial for text-independent speaker identification tasks. In this paper, the performance of various dimensionality reduction techniques is evaluated for speaker identification systems using Gaussian Mixture Model (GMM) as the statistical classifier. An enhancement of the standard Linear Discriminant Analysis (LDA) is proposed in which class distributions are assumed to follow Gaussian Mixture distribution. This assumption is more appropriate for asymmetric and multimodal class conditional densities. In addition, a new feature selection technique based on the QR factorization method is introduced. Computer simulation results reveal that the proposed modification to the LDA outperforms the standard algorithm in terms of classification accuracy. Moreover, the QR-based selection technique produces comparable results to other prominent dimensionality reduction techniques.
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