Cognitive radio engine learning adaptation

Abstract

Cognitive radio (CR) system being an intelligence-based communication device has been considered as the next generation emerging technologies to Wireless Communication Systems (WCS). This CR's embedded-intelligent agent is called Cognitive Engine (CE), and is responsible for the dynamic adaptation between the WCS's environment and the radio operational parameters. As a result of CR's intelligence capability, the WCS's quality of service (QoS) and its connectivity operations get enhanced. In order to evaluate the CR engine performance in respect to its learning, timing, and its computational performances. This paper proposes an alternative state-of-the-art enhanced CR learning engine based on Random Neural Network (RNN). Unlike Artificial Neural Network (ANN) systems, RNN establishes strong data generalization, converges faster and produces relatively smaller levels of prediction errors. Subjected to similar environmental conditions, the simulation cumulative results show that the performance of the proposed RNN system is satisfactory and produces 36.895% performance improvement above the ANN learning engine.