Context Augmented Spectrum Sensing in Cognitive Radio Networks
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Spectrum management has become a crucial issue in wireless networks. However, optimal utilization of the spectrum among the different users is not a trivial task. Over the last two decades, wireless communication has witnessed a significant increase in applications. However, fixed strategies for allocating the spectrum bands cannot handle multiple requirements simultaneously, which is a core requirement of the emerging wireless applications. More importantly, licensed users or primary users (PUs) in wireless networks are intermittently connected, leading to spectrum underutilization. All of these limitations make it imperative that efficient strategies be developed to manage the spectrum among multiple users or networks. Cognition as a component of intelligence has been employed in communication technologies such as CR Networks for reasoning and learning goals. From this perspective, a Cognitive Radio Network is an adaptive data network that applies cognition as an optimization tool aiming to optimize spectrum sharing among multiple secondary users (SUs) in addition to the PUs in an autonomous and dynamic way. Spectrum Sensing is an important element of Cognitive Radio technology since its outcome is the basis for all the subsequent stages of the cognition cycle. However, with stand-alone Cognitive Radio devices, local spectrum sensing techniques such as Energy Detection technique might draw a false conclusion about the presence of a primary transmitter due to several reasons (e.g. fading, shadowing, hidden node problem, noise uncertainty, etc). Cooperative sensing minimizes the uncertainty due to those factors by exploiting the spatial variation of SUs, then concludes one global decision about the PU's presence/absence. In this research work, I propose an intelligent cooperative spectrum sensing system whereby the contextual information of each secondary user is augmented in the fusion process wherein a set of information acquired by several contributing SUs are fused to optimize a global decision. Incorporating the contextual information of the SUs improves the spectrum sensing decision's reliability in the sense that false rejections and false acceptances are minimized, and therefore utilization is optimized. Artificial Neural Networks, as a Machine Learning and Artificial Intelligence tool, has been employed as a fusion algorithm utilizing the context of every SU to optimize final decisions. Experimental work is reported and discussed to demonstrate the effectiveness of the proposed technique.