A Hybrid Cognitive Engine for Improving Coverage in 3G Wireless Networks

Abstract

Third generation (3G) wireless networks have been well studied and optimized with traditional radio resource management techniques, but still there is room for improvement. Cognitive radio (CR) technology can bring significant network improvements by providing awareness to the surrounding radio environment, exploiting previous network knowledge and optimizing the use of radio resources using machine learning and artificial intelligence techniques. Cognitive radio can also co-exist with legacy equipment thus acting as a bridge among heterogenous communication systems. In this paper, we present a hybrid cognitive radio engine for 3G wireless networks. The engine is designed using case-based reasoning (CBR) and decision tree (DT) searches, as the main blocks to the engine's reasoning, learning and optimization functions. The engine model was implemented and tested via simulation, it was applied to improve coverage in the network.