Artificial Neural Networks-Based Spectrum Sensing Algorithms for Cognitive Radio Based Disaster Response Networks CR-DRNs - F. AlAqad, Khaled; A. Burhanuddin, M.; Binti Harum, Norharyati (Opción, 2019)
The recent advancements of artificial intelligence techniques and its impact in the context of cognitive radio networks has become immeasurable. Artificial intelligence redefines and empowers the decision making and logical capa- bility of computing machines, one of the most Significant functionalities of artificial intelligence include spectrum sensing and management which is a key function of Cognitive radio Cognitive Radio (CR) networks, these net- works empower secondary users to work on licensed spectrum or primary user’s spectrum without any interference , the implementation of CR for Dis- aster Response Networks (DRNs) imposes the necessity of sophisticated and advanced techniques that explore the available spectrum and utilizes it in such a way that guarantees maximum spectrum utilization without any interfer- ence with PUs licensed spectrum and provide communication solutions within the 48 or 72 hours following the occurrence of disastrous event, this is the primary concern of Artificial Neural Networks (ANNs) based algorithms that are capable of understanding the environment, learning and adjusting in real time operating parameter according to specific need of unlicensed user, in this study , we highlight the most recent and applicable algorithms of (ANNs) and show how they can be implemented in CR-DRNs to efficiently sense and utilize the spectrum in disaster area where available resources are limited and rapid deployment of a reliable communication system is must.