@inproceedings{8fe37fd89c664d9e8ae56386e95c7456,
title = "Hybrid cognitive engine for radio systems adaptation",
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.",
keywords = "Case-based reasoning Software-defined radio (SDR), Cognitive engine, Decision-trees",
author = "Ismail Alqerm and Basem Shihada",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 14th IEEE Annual Consumer Communications and Networking Conference, CCNC 2017 ; Conference date: 08-01-2017 Through 11-01-2017",
year = "2017",
month = jul,
day = "17",
doi = "10.1109/CCNC.2017.7983233",
language = "English (US)",
series = "2017 14th IEEE Annual Consumer Communications and Networking Conference, CCNC 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "778--783",
booktitle = "2017 14th IEEE Annual Consumer Communications and Networking Conference, CCNC 2017",
address = "United States",
}