MIMO-Orthogonal space time block code system joint with BCH-TC in Rayleigh fading channel for performance of transmit antenna selection

Purpose To enhance the performance transmit antenna selection (TAS) of spatial modulation (SM), systems technique needs to be essential. This TAS is an effective technique for reducing the multiple input multiple output (MIMO) systems computational difficulty, and bit error rate (BER) can increase remarkably by various TAS algorithms. But these selection methods cannot provide code gain, so it is essential to join the TAS with external code to obtain cy -ode gain advantages in BER. Design/methodology/approach In this paper, Bose–Chaudhuri–Hocquenghem (BCH)-Turbo code TC is combined with the orthogonal space time block code system. Findings In some existing work, the improved BER has been perceived by joining forward error correction code and space time block code (STBC) for MIMO systems provided greater code gain. The proposed work can provide increasing code gain and the effective advantages of the TAS-OSTBC system. Originality/value To perform the system analysis, Rayleigh channel is used. In the case with multiple TAS-OSTBC systems, better performance can provide by this new joint of the BCH-Turbo compared to the conventional Turbo code for the Rayleigh fading.

Improved Pair-Wise Detections of Differential Quasi-Orthogonal Space-Time Modulation with Four Transmit Antennas

In this paper, we propose new complex and real pair-wise detection for conventional differential space–time modulations based on quasi-orthogonal design with four transmit antennas for general QAM. Since the new complex and real pair-wise detections allow the independent joint ML detection of two complex and real symbol pairs, respectively, the decoding complexity is the same as or lower than conventional differential detections. Simulation results show that the proposed detections exhibit almost identical performance with an optimum maximum-likelihood receiver, as well as improved performance compared with conventional pair-wise detections, especially for higher modulation order.

Normality Testing of High-Dimensional Data Based on Principle Component and Jarque–Bera Statistics

The testing of high-dimensional normality is an important issue and has been intensively studied in the literature, it depends on the variance–covariance matrix of the sample and numerous methods have been proposed to reduce its complexity. Principle component analysis (PCA) has been widely used in high dimensions, since it can project high-dimensional data into a lower-dimensional orthogonal space. The normality of the reduced data can then be evaluated by Jarque–Bera (JB) statistics in each principle direction. We propose a combined test statistic—the summation of one-way JB statistics upon the independence of the principle directions—to test the multivariate normality of data in high dimensions. The performance of the proposed method is illustrated by the empirical power of the simulated normal and non-normal data. Two real data examples show the validity of our proposed method.

Normality Testing of High-Dimensional Data Based on Principle Component and Jarque-Bera Statistics

The testing of high-dimensional normality has been an important issue and has been intensively studied in literatures, it depends on the Variance-Covariance matrix of the sample, numerous methods have been proposed to reduce the complex of the Variance-Covariance matrix. The principle component analysis(PCA) was widely used since it can project the high-dimensional data into lower dimensional orthogonal space, and the normality of the reduced data can be evaluated by Jarque-Bera(JB) statistic on each principle direction. We propose two combined statistics, the summation and the maximum of one-way JB statistics, upon the independency of each principle direction, to test the multivariate normality of data in high dimensions. The performance of the proposed methods is illustrated by the empirical power of the simulated data of normal data and non-normal data. Two real examples show the validity of our proposed methods.