Intelligence Measure of Cognitive Radios with Learning Capabilities
Monireh Dabaghchian, Songsong Liu, Amir Alipour-Fanid, Kai Zeng, Xiaohua Li, Yu Chen · 2016
Cognitive radio (CR) is considered as a key enabling technology for dynamic spectrum access to improve spectrum efficiency. Although the CR concept was invented with the core idea of realizing "cognition", the research on measuring CR cognition capabilities and intelligence is largely open. Deriving the intelligence capabilities of CR not only can lead to the development of new CR technologies, but also makes it possible to better configure the networks by integrating CRs with different intelligence capabilities in a more cost- efficient way. In this paper, for the first time, we propose a data-driven methodology to quantitatively analyze the intelligence factors of the CR with learning capabilities. The basic idea of our methodology is to run various tests on the CR in different spectrum environments under different settings and obtain various performance results on different metrics. Then we apply factor analysis on the performance results to identify and quantize the intelligence capabilities of the CR. More specifically, we present a case study consisting of sixty three different types of CRs. CRs are different in terms of learning-based dynamic spectrum access strategies, number of sensors, sensing accuracy, and processing speed. Based on our methodology, we analyze the intelligence capabilities of the CRs through extensive simulations. Four intelligence capabilities are identified for the CRs through our analysis, which comply with the nature of the tested algorithms.