An Operation Reduction Using Fast Computation of an Iteration-Based Simulation Method with Microsimulation-Semi-Symbolic Analysis

Abstract

This paper presents a method for shortening the computation time and reducing the number of math operations required in complex calculations for the analysis, simulation, and design of processes and systems. The method is suitable for education and engineering applications. The efficacy of the method is illustrated with a case study of a complex wireless communication system. The computer algebra system (CAS) was applied to formulate hypotheses and define the joint probability density function of a certain modulation technique. This innovative method was used to prepare microsimulation-semi-symbolic analyses to fully specify the wireless system. The development of an iteration-based simulation method that provides closed form solutions is presented. Previously, expressions were solved using time-consuming numerical methods. Students can apply this method for performance analysis and to understand data transfer processes. Engineers and researchers may use the method to gain insight into the impact of the parameters necessary to properly transmit and detect information, unlike traditional numerical methods. This research contributes to this field by improving the ability to obtain closed form solutions of the probability density function, outage probability, and considerably improves time efficiency with shortened computation time and reducing the number of calculation operations.

A Symbolic Encapsulation Point as Tool for 5G Wideband Channel Cross-Layer Modeling

“…Thanks to the concept of microsimulation semi-symbolic analysis [ 36 ], all variables and operations are encapsulated in one framework, which is accessed by simply calling its reference. The encapsulation process is the store of all relevant parameters, data, and behavior in one component—a symbolic encapsulation point.…”

“…The paper [ 39 ] shows how it is possible to speed up a complex calculation where the relative errors do not exceed more than 8% between the original and the approximate value. In [ 36 ], the calculation speed was taken into account, which speeds up the calculation of outage probability by 955 times, and reduces the number of mathematical operations by almost 4 times, while for statistical parameters of the second-order, LCR is accelerated by 20 times and AFD by 15 × 10 3 times, for a relative error ranging between 0.5% and 2.5%.…”

Symbolic Entropy Analysis and Its Applications

“…In the last paper published in the Special Issue, Mladenovic et al [ 24 ] have presented the use of symbolic processing to reduce the number of calculation operations in iteration-based simulation methodologies, as well as to accelerate their computation. The proposed algorithm was validated on two examples—the computation of non-coherent amplitude shift keying with shadowing, interference, and correlated noise; and the estimation of second-order statistics in wireless channels.…”