Minimization of Handover Decisions with Quality of Service Using Fuzzy Logic Prediction Model

Abstract

Artificial Intelligence (AI) based network technologies considered best method to enhance the Quality of Service (QoS) of handoff algorithms due to its ability to handle huge data in fast processing. It helps to take effective handoff decision based on Received Signal Strength (RSS), traffic intensity, speed and diversity. In this paper the fuzzy logic prediction model has been developed for handoff decisions. On retrieving the network, the RSS was developed to form a time series data over a period of time. The data is then proceeded with the newly proposed fuzzy logic prediction model for estimation and prediction coefficients, while the predicted values of RSS are organized as fuzzy sets and in conjunction with other measured parameters of network. Moreover, the Received Signal Strength Indicator (RSSI), traffic load in the network, channel capacity, network load (NL), Bit Error Rate (BER), received signal power level has been estimated throughput the Signal to Noise Ratio (SNR), In addition, to user preferences such as the security and cost of the network. The overall performance of proposed fuzzy logic prediction model is capable to predict the handover decision ahead then the available RSS method and other handover necessity estimation techniques. This model also reduces the ping-pong effect associated with other techniques of handover.