Show simple item record

dc.contributor.authorAlQerm, Ismail
dc.contributor.authorShihada, Basem
dc.date.accessioned2019-12-16T13:37:10Z
dc.date.available2019-12-16T13:37:10Z
dc.date.issued2019-11-01
dc.identifier.citationAlQerm, I., & Shihada, B. (2019). Enhanced Online Q-Learning Scheme for Energy Efficient Power Allocation in Cognitive Radio Networks. 2019 IEEE Wireless Communications and Networking Conference (WCNC). doi:10.1109/wcnc.2019.8885623
dc.identifier.doi10.1109/WCNC.2019.8885623
dc.identifier.urihttp://hdl.handle.net/10754/660617
dc.description.abstractThe considerable growth in demands for wireless services have led to spectrum scarcity challenge. Cognitive radio came into practice to deal with the scarcity problem by granting cognitive users access to the licensed spectrum. However, this solution requires efficient power allocation strategies to guarantee QoS for cognitive system, reduce power consumption, and protect primary users from the cognitive users' interference impact. In this paper, we investigate the energy efficient power allocation problem for cognitive radio networks in underlay mode. We propose a novel approximated online Q-learning scheme for power allocation in which cognitive users learn with conjecture feature to select the most appropriate power level. The power allocation problem is formulated as an optimization problem with the goal to maximize energy efficiency under QoS and interference constraints. The scheme is evaluated using software defined radio testbed and simulations. The evaluation results demonstrate the scheme capability to guarantee SINR for both primary and cognitive systems and mitigate interference with minimum power consumption in comparison with other schemes.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttps://ieeexplore.ieee.org/document/8885623/
dc.rightsArchived with thanks to IEEE
dc.titleEnhanced Online Q-Learning Scheme for Energy Efficient Power Allocation in Cognitive Radio Networks
dc.typeConference Paper
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.conference.date2019-04-15 to 2019-04-19
dc.conference.name2019 IEEE Wireless Communications and Networking Conference, WCNC 2019
dc.conference.locationMarrakesh, MAR
dc.eprint.versionPost-print
dc.contributor.institutionDept. of Mathematics and Computer Science University of Missouri, St. Louis, USA
kaust.personShihada, Basem
refterms.dateFOA2019-12-16T13:43:02Z
dc.date.published-online2019-11-01
dc.date.published-print2019-04


Files in this item

Thumbnail
Name:
Conference Paperfile1.pdf
Size:
618.2Kb
Format:
PDF
Description:
Post-print

This item appears in the following Collection(s)

Show simple item record