Adaptive neural-fuzzy controllers for dynamic channel borrowing in wireless cellular networks
A neural-fuzzy controller for the dynamic channel-borrowing scheme (NFDCBS) is presented to provide multimedia services and to support the increasing number of users. In a cellular network, the calls arrival rate, the call duration, and the communication overhead between the base station and the control center are vague and uncertain. Therefore, we propose a new novel efficient dynamic channel borrowing for load balancing in distributed cellular networks based on neural-fuzzy controllers scheme. The proposed scheme exhibits better learning abilities, optimization abilities, robustness, and fault-tolerant capability thus yielding better performance compared with the other algorithms. It aims to efficiently satisfy their diverse quality of service (QoS) requirements. The results show that our algorithm has lower blocking rate, lower dropping rate, less update overhead, and shorter channel acquisition delays than the previous methods.