Optimized Coefficients for the Generalized Karagiannidis–Lioumpas Approximations and Bounds to the Gaussian Q-Function

<p>This is a supplementary&nbsp;dataset for&nbsp;the publication:</p> <p>I. M. Tanash and T. Riihonen, &quot;Generalized Karagiannidis&ndash;Lioumpas Approximations and Bounds to the Gaussian Q-Function with Optimized Coefficients,&quot; in<em> IEEE Communications Letters</em>, in press.</p> <p>The dataset contains the sets of the optimized coefficients for the novel GKL minimax approximations and bounds of the Gaussian Q-function, and the optimized coefficients for the GKL approximations in terms of the total error. The corresponding&nbsp;optimized coefficients are found up to 10 terms (N=10) for the two variations of the absolute error and for the relative error in terms of the minimax and the total errors.</p> <p>The Matlab function (func_extract_coef.m)&nbsp;extracts the required set of optimal coefficients from the provided dataset&nbsp;according to the selected optimization_criterion, error_type, number of terms, the bound or approximation type, and the variation. See help&nbsp;func_extract_coef for more information.</p> <p>A Matlab script (Example.m) is also provided as an example to illustrate&nbsp;the use&nbsp;of the provided&nbsp;Matlab function in extracting the required coefficients from the dataset, to calculate and plot the corresponding minimax absolute error function which is shown by&nbsp;figure&nbsp;Example.jpg. Another example is given in the same script to extract the coefficients of the total relative error.<br> &nbsp;</p>