M
Mario Bkassiny
Researcher at University of New Mexico
Publications - 29
Citations - 1017
Mario Bkassiny is an academic researcher from University of New Mexico. The author has contributed to research in topics: Cognitive radio & Partially observable Markov decision process. The author has an hindex of 15, co-authored 28 publications receiving 923 citations. Previous affiliations of Mario Bkassiny include State University of New York at Oswego & Lebanese American University.
Papers
More filters
Journal ArticleDOI
A Survey on Machine-Learning Techniques in Cognitive Radios
TL;DR: The learning problem in cognitive radios (CRs) is characterized and the importance of artificial intelligence in achieving real cognitive communications systems is stated and the conditions under which each of the techniques may be applied are identified.
Journal ArticleDOI
Asymmetric Cooperative Communications Based Spectrum Leasing via Auctions in Cognitive Radio Networks
TL;DR: This paper proposes and analyzes both a centralized and a distributed decision-making architecture for the secondary CRN and formulate an auction game-based protocol in which each SU independently places bids for each primary channel and receivers of each primary link pick the bid that will lead to the most power savings.
Journal ArticleDOI
Reconfigurable front-end antennas for cognitive radio applications
Youssef Tawk,Mario Bkassiny,Georges El-Howayek,Sudharman K. Jayaweera,Keith Avery,Christos G. Christodoulou +5 more
TL;DR: In this paper, two different techniques to achieve the required frequency agility are proposed, one based on a rotational motion of the radiating patch and the second based on optical switching.
Journal ArticleDOI
Wideband Spectrum Sensing and Non-Parametric Signal Classification for Autonomous Self-Learning Cognitive Radios
TL;DR: This paper presents an autonomous cognitive radio architecture, referred to as the Radiobot, which applies a blind energy detection followed by a cyclostationary detection method to detect the active signals and extract their underlying periodic properties as reflected in cyclic frequencies.
Proceedings ArticleDOI
Distributed Reinforcement Learning based MAC protocols for autonomous cognitive secondary users
TL;DR: A distributed Reinforcement Learning algorithm is developed that allows each autonomous cognitive radio to distributively learn its own spectrum sensing policy, and enables secondary users to non-cooperatively reach an equilibrium that leads to high utilization of idle channels while minimizing the collisions among secondary cognitive radios.