OPTIMAL COMPRESSOR FUNCTION APPROXIMATION UTILIZING Q-FUNCTION APPROXIMATIONS

In this paper, we have proposed two solutions for approximating the optimal compressor function for the Gaussian source. Both solutions are based on approximating Q-function with exponential functions. These solutions differ in that the second one is given in parametric form and can be considered as a more general solution compared to the first one, which is a special case of the second solution for a specific value of the mentioned parameter. The approximated functions proposed in the paper facilitate designing scalar companding quantizers for the Gaussian source since with the application of these functions main difficulties occurred in designing the observed quantizers for the Gaussian source can be overcome

NOVEL EXPONENTIAL TYPE APPROXIMATIONS OF THE Q-FUNCTION

In this paper, we propose several solutions for approximating the Q-function using one exponential function or the sum of two exponential functions. As the novel Q-function approximations have simple analytical forms and are therefore very suitable for further derivation of expressions in closed forms, a large number of applications are feasible. The application of the novel exponential type approximations of the Q-function is especially important for overcoming issues arising in designing scalar companding quantizers for the Gaussian source, which are caused by the non-existence of a closed form expression for the Q-function. Since our approximations of the Q-function have simple analytical forms and are more accurate than the approximations of the Q-function previously used for the observed problem in the scalar companding quantization of the Gaussian source, their application, especially for this problem is of great importance

Simple Solution for Designing the Piecewise Linear Scalar Companding Quantizer for Gaussian Source

To overcome the difficulties in determining an inverse compressor function for a Gaussian source, which appear in designing the nonlinear optimal companding quantizers and also in the nonlinear optimal companding quantization procedure, in this paper a piecewise linear compressor function based on the first derivate approximation of the optimal compressor function is proposed. We show that the approximations used in determining the piecewise linear compressor function contribute to the simple solution for designing the novel piecewise linear scalar companding quantizer (PLSCQ) for a Gaussian source of unit variance. For the given number of segments, we perform optimization procedure in order to obtain optimal value of the support region threshold which maximizes the signal to quantization noise ratio (SQNR) of the proposed PLSCQ. We study how the SQNR of the considered PLSCQ depends on the number of segments and we show that for the given number of quantization levels, SQNR of the PLSCQ approaches the one of the nonlinear optimal companding quantizer with the increase of the number of segments. The presented features of the proposed PLSCQ indicate that the obtained model should be of high practical significance for quantization of signals having Gaussian probability density function

Razvoj metoda i algoritama za procenu performansi komunikacionih sistema primenom aproksimacija specijalnih funkcija

The intensive development of wireless communication systems has been accompanied by the need to develop methods and algorithms for implementing appropriate approximations of special functions in order to efficiently estimate the corresponding performance of these services through their application. In order to evaluate the behavior of digital communications systems, it is necessary to estimate standard performance measures for the observed wireless communications systems, various modulation types application, detection types, as well as channel models, and observe relations between performance and key values of system parameters. The analysis of the average bit error rate at reception for the applied modulation format is one of the tools for assessing service performance, that describes the nature of the wireless communication system in the best manner. In order to analytically evaluate the average bit error rate for the applied modulation format, it is necessary to perform the most accurate implementation of the approximation of special functions erfc(x), erf (x), Marcum Q, in the widest input range values. The dissertation will present composite methods of the special functions’ approximations. In addition to the simplicity of realization in approximating the observed functions, the aspect of robustness of approximations absolute and relative error values in a wide range of input parameters values will be considered. The advantages of the proposed solutions will be highlighted by direct comparison with the absolute and relative errors obtained by using the known special functions’ approximations from the literature. Furthermore, when transferring information through fading communication channels, for cases of application of proposed special functions’ approximations, it will be proved that system performance can be determined more easily by applying solutions proposed in the dissertation. In this way, it would be easier to determine the probability of the error of communication systems due to different types of fading existance in the channel. By comparing predicted values of the average bit error rate at reception, when transmitting signals through various communication channels medias, for cases of application of existing, previously proposed special functions’ approximations, with the average bit error rate at reception obtained by calculation based on the proposed approximation solutions, it will be shown that communication performances can be calculated more precisely. Proposed approximations could also be used in the source coding of the signal and could simplify design and realization of the quantizers