Experience level analysis for a cognitive radio engine

A cognitive radio engine (CE) is where the advanced adaptation algorithms for a cognitive radio is implemented. A CE is an intelligent agent which observes the radio environment and chooses the best communication settings that meet the application’s goal. In this process, providing reliable performance is one of the major challenges faced by a CE. Therefore, one of the most important issues in designing CEs is the ability to characterize and reliably predict performance of the CE in different operating scenarios. An operating scenario is defined as the set of the operating objective, channel availability, and channel quality metrics. In this paper, we develop several performance evaluation and prediction indices to quantify the amount of knowledge of different CE algorithms independently of the implementation approach and/or their operating scenarios. Using these new indices, we are able to provide a more accurate estimation of the learning process and future performance of each individual CE algorithm. A number of simulation-based experiments was conducted. Our results show that proposed contextual CE algorithms based on the developed knowledge indicators is able to improve the wireless communication system’s objective rewards significantly. In effect, the contextual CE is able to deliver about 10% more data than the CE with the fixed exploration rate.