Dynamic Load Balancing for Congestion Avoidance using Adaptive Neuro-Fuzzy Inference System in Mobile Communication Network

Abstract

Network congestion is one of the key challenges to mobile communication services. This is because of rapid and constant growth in the mobile subscriber base that has led to an increase in network traffic. The more the network traffic the more economic opportunity from the business perspective for the operators and the challenges it poses on the network. If not followed by network capacity expansion it will defiantly pose a serious network issue like network congestion that leads to call drop and poor Quality of Service (QoS). Many research works adopted handover mechanism as a means of reducing congestion in a network. However, the decision on when to initiate a handover, which cell to receive the Mobile Station (MS) and how to ensure that the QoS requirements are maintained are the paramount research questions that must be resolved. Therefore, a handover process using soft computing in the Adaptive Neuro-Fuzzy Inference System (ANFIS) to ensure balanced traffic load distribution in-network and reduce the probability of congestion is proposed. The study allows simultaneous evaluation of three major network parameters Received Signal Strength (RSS), Received Signal Quality (RxQual) and network traffic using ANFIS to improve system performance. The results show that when the Hysteresis value approaches 6 the handover processes are triggered. The hysteresis value of the concerned MS with neighbouring cells is considered to determine the most suitable cell to handover. This work will be able to achieve dynamic load balancing, congestion avoidance and avoided the ‘ping pong’ effect that is often an issue with handover with less computation. At the end customer satisfaction will be achieve Quality of experience (QoE).