Type
Conference PaperAuthors
Alqerm, Ismail
Shihada, Basem

KAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionComputer Science Program
Date
2017-07-20Online Publication Date
2017-07-20Print Publication Date
2017-01Permanent link to this record
http://hdl.handle.net/10754/625990
Metadata
Show full item recordAbstract
Network efficiency and proper utilization of its resources are essential requirements to operate wireless networks in an optimal fashion. Cognitive radio aims to fulfill these requirements by exploiting artificial intelligence techniques to create an entity called cognitive engine. Cognitive engine exploits awareness about the surrounding radio environment to optimize the use of radio resources and adapt relevant transmission parameters. In this paper, we propose a hybrid cognitive engine that employs Case Based Reasoning (CBR) and Decision Trees (DTs) to perform radio adaptation in multi-carriers wireless networks. The engine complexity is reduced by employing DTs to improve the indexing methodology used in CBR cases retrieval. The performance of our hybrid engine is validated using software defined radios implementation and simulation in multi-carrier environment. The system throughput, signal to noise and interference ratio, and packet error rate are obtained and compared with other schemes in different scenarios.Citation
AlQerm I, Shihada B (2017) Hybrid cognitive engine for radio systems adaptation. 2017 14th IEEE Annual Consumer Communications & Networking Conference (CCNC). Available: http://dx.doi.org/10.1109/CCNC.2017.7983233.Conference/Event name
14th IEEE Annual Consumer Communications and Networking Conference, CCNC 2017Additional Links
http://ieeexplore.ieee.org/document/7983233/ae974a485f413a2113503eed53cd6c53
10.1109/CCNC.2017.7983233