An Iteration-Based Simulation Method for Getting Semi- Symbolic Solution of Non-coherent FSK/ASK System by Using Computer Algebra Systems

Abstract

Abstract:The paper presents a development of an IBSM (Iteration-Based Simulation Method) to obtain semi-symbolic solutions of digital telecommunication non-coherent FSK/ASK system by using computer algebra systems. These solutions are applied for derivation of the symbol error probability (SEP) needed for quantitative and qualitative analysis of performances of telecommunication systems in optimization and design of low-complexity implementation into highcomplexity structures. Various software tools, used in telecommunications for calculating, designing and analysing, have been developed from the viewpoint of numeric-only algorithms. But, many shortcomings cannot be neglected, such as generating of a great amount of numeric data, losing insight into the phenomenon of investigation, and numerical computation manipulates with numerical values. For these reasons, IBSM provides design, optimization, manipulation, and simplification by introducing a new parameter of iteration. The IBSM is applied to aforementioned system when the uncorrelated and correlated noise and the interferences are present in both upper and lower frequencies, respectively. The impact of number of iterations, number of bits, correlation coefficient and different values of interferences to symbol error probability are derived on semi-symbolic forms. Making use of IBSM gives an opportunity to solve very complex analyses and derive many sophisticated conclusions.

An Operation – Reduced Calculation Method for Solving Complex Communication Systems

“…nonlinear system were reported in number of journal papers [8], [9]. Wolfram language is also available on cheap Raspberry Pi hardware environment with additional general-purpose input/output (GPIO) programming and serial communication that are of great importance for internet of things applications.…”

“…Assumption is to exist one dominant signal and multipath, so, the consequence is Rician distribution; 2. transformation of the expressions and calculation the probability distribution function of errors probability using an iterative-reduced simulation method [9], [10]; 3. defining the approximate function and calculate the coefficients; 4. calculation of the error function between the exact value of the error probability and the approximate function; 5. calculation of the optimal number of operations.…”

“…A large number of iterations follows a large number of operations during calculation, so, it can take a time-consuming [8]. We use similar iterative method as in [9], [10] to get error probability function. In the following, we use the label P e for the original total error probability, and P e,approx for the approximative total error probability.…”

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

“…Engineers and researchers may not have appropriate insight into the impact of the parameters necessary for the effective investigation or design. Additionally, the numerical computation generates a large amount of data, which may sometimes lead to erroneous results [ 10 , 11 ]. These incorrect results may be due to the finite word length in the records, or errors occurring during the shortening of numbers in fractions, for example.…”

“…These issues have been overcome by introducing a new method that treats variables and parameters as symbols. The method is called the iteration-based simulation method (IBSM) [ 11 , 12 ]. In addition to the IBSM that provides symbolic analysis, we allow the analysis to be partially observed through the concept of microsimulation analysis, which additionally enhances the ability to influence the parameters and variables.…”