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Network congestion control remains important and of high priority, especially given the growing size, demand, and speed of cellular networks. Recent research in this area has come up with Capacity and Coverage Optimization techniques based on Self Organizing Networks (SONs). A major issue tackled by SONs is the irregular and frequently changing distribution of cellular subscribers both in time and area. As existing network technologies continue to evolve, more and more tunable parameters and various customizations are being introduced. This has resulted in a rise in complexity that is making traditional manual optimization challenging. On the other hand, even though current Self-Organizing Networks (SON) focus is on Long Term Evolution (LTE) and beyond, legacy networks such as 3G are adopting different SON based solutions to address the above mentioned challenges in other radio access network (RAN) types. In this paper the problem of capacity optimization in 3G Cellular Networks is addressed using Rule-Based Fuzzy Logic to control the CPICH power and as a result improve the cell capacity. One of the reasons for choosing Fuzzy Logic Controllers (FLC) is its logical resemblance to a human operator. It operates on the foundations of a knowledge base derived from an expert operator’s knowledge. The autonomous operation is aimed at reducing the frequent attention and effort required by a radio optimization engineer to carry out capacity optimization tasks which are currently done mostly manually. The FLC will be involved in the detection of high load 3G cells that do not have enough cell resources available and could benefit from CPICH power adjustment as a radio optimization engineer would normally do manually. Simulation results show that the CPICH power control system based on fuzzy achieves a significant improvement in the Downlink cell utilization which in turn improves the cell performance compared to a constant CPICH

Common Pilot Channel Power Control for UMTS Cellular Networks Traffic Load Balancing based on Fuzzy Logic Control