A Weight Influence of Logarithmic and Exponential Functions on the Selection of Wireless Networks using Multi-Criteria Decision-Making Methods

This research aims to study the influence of logarithmic and exponential functions on the multi-criteria decision-making algorithm that changes linear method to nonlinear method. It is carried out to better understand the multi-criteria decision-making, namely the technique for preference by similarity to ideal solution (TOPSIS) algorithm whereby in which these functions may influence the criteria weights during the selection of the best network. The investigation is applied under different network types to evaluate the most optimum network that leads to better throughput, low latency, minimum BER, and low price per MB. The algorithms are assessed in MATLAB simulation environments. The study also considered the adoption of the Wi-Fi networks standard which is factors such as bandwidth, signal to noise ratio and the channel modulation technique were determined during the decision-making process. The simulation results show that the exponential function had produced approximately similar results to that of linear TOPSIS algorithm because both methods keep the weights to demonstrate positive values. However, logarithmic TOPSIS produced different results as the weights have negative values which lead to a phase shift of 180⁰ during the decision process. Thus, linear TOPSIS was found to have the best results while logarithmic TOPSIS had the worst outcome.