Hybrid cognitive engine for radio systems adaptation

Ismail Alqerm, Basem Shihada

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

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.
Original languageEnglish (US)
Title of host publication2017 14th IEEE Annual Consumer Communications & Networking Conference (CCNC)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages778-783
Number of pages6
ISBN (Print)9781509061969
DOIs
StatePublished - Jul 20 2017

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