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

Proposal of Simple and Accurate Two-Parametric Approximation for the Q

The approximations for the Q-function reported in the literature so far have mainly been developed to overcome not only the difficulties, but also the limitations, caused in different research areas, by the nonexistence of the closed form expression for the Q-function. Unlike the previous papers, we propose the novel approximation for the Q-function not for solving some particular problem. Instead, we analyze this problem in one general manner and we provide one general solution, which has wide applicability. Specifically, in this paper, we set two goals, which are somewhat contrary to each other. The one is the simplicity of the analytical form of Q-function approximation and the other is the relatively high accuracy of the approximation for a wide range of arguments. Since we propose a two-parametric approximation for the Q-function, by examining the effect of the parameters choice on the accuracy of the approximation, we manage to determine the most suitable parameters of approximation and to achieve these goals simultaneously. The simplicity of the analytical form of our approximation along with its relatively high accuracy, which is comparable to or even better than that of the previously proposed approximations of similar analytical form complexity, indicates its wide applicability

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

Projektovanje kvantizera za primenu u obradi signala i neuronskim mrežama

Scalar quantizers are present in many advanced systems for signal processing and transmission, аnd their contribution is particular in the realization of the most important step in digitizing signals: the amplitude discretization. Accordingly, there are justified reasons for the development of innovative solutions, that is, quantizer models which offer reduced complexity, shorter processing time along with performance close to the standard quantizer models. Designing of a quantizer for a certain type of signal is a specific process and several new methods are proposed in the dissertation, which are computationally less intensive compared to the existing ones. Specifically, the design of different types of quantizers with low and high number of levels which apply variable and a fixed length coding, is considered. The dissertation is organized in such a way that it deals with the development of coding solutions for standard telecommunication signals (e.g. speech), as well as other types of signals such as neural network parameters. Many solutions, which belong to the class of waveform encoders, are proposed for speech coding. The developed solutions are characterized by low complexity and are obtained as a result of the implementation of new quantizer models in non-predictive and predictive coding techniques. The target of the proposed solutions is to enhance the performance of some standardized solutions or some advanced solutions with the same/similar complexity. Testing is performed using the speech examples extracted from the well-known databases, while performance evaluation of the proposed coding solutions is done by using the standard objective measures. In order to verify the correctness of the provided solutions, the matching between theoretical and experimental results is examined. In addition to speech coding, in dissertation are proposed some novel solutions based on the scalar quantizers for neural network compression. This is an active research area, whereby the role of quantization in this area is somewhat different than in the speech coding, and consists of providing a compromise between performance and accuracy of the neural network. Dissertation strictly deals with the low-levels (low-resolution) quantizers intended for post-training quantization, since they are more significant regarding compression. The goal is to improve the performance of the quantized neural network by using the novel designing methods for quantizers. The proposed quantizers are applied to several neural network models used for image classification (some benchmark dataset are used), and as performance measure the prediction accuracy along with SQNR is used. In fact, there was an effort to determine the connection between these two measures, which has not been investigated sufficiently so far