Fuzzy Neural Networks for Channel Management in Heterogeneous Wireless Cellular Networks

In this paper, adaptive channel management approach fuzzy neural networks in heterogeneous wireless cellular networks (ACM-FNN) is presented to efficient resource allocation, and admission control schemes are needed to guarantee quality-of-service (QoS) for differentiated services. The channel management in a two-tier such as micro cell or macro cell wireless cellular networks. Effective reliability and efficiently schemes are also needed to make network services more reliable and efficient. In a wireless cellular networks for uneven traffic load in a cellular system may occur creating a hot spots. So the two-tier wireless cellular system should be able to cope with such traffic load in certain cells. In a cellular network, the calls arrival rate, the call duration , the mobility speed and the communication overhead between the base station and the mobile switch center are vague and uncertain. Therefore, we propose a new efficient channel-borrowing scheme in heterogeneous distributed cellular networks based on ACM-FNN. The proposed scheme exhibits better learning abilities, optimization abilities, robustness, and fault-tolerant capability thus yielding better performance compared with other algorithms. The results show that our algorithm has lower call blocking rate, lower call dropping rate, less update messages overhead, and shorter channel acquisition delays. Key-Words: Dynamic channel borrowing, dynamic load balancing, fuzzy neural networks, channel management, heterogeneous wireless cellular networks.