Hybrid cognitive engine for radio systems adaptation

Ismail Alqerm; Basem Shihada

doi:10.1109/CCNC.2017.7983233

Hybrid cognitive engine for radio systems adaptation

Ismail Alqerm, Basem Shihada

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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 language	English (US)
Title of host publication	2017 14th IEEE Annual Consumer Communications and Networking Conference, CCNC 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	778-783
Number of pages	6
ISBN (Electronic)	9781509061969
DOIs	https://doi.org/10.1109/CCNC.2017.7983233
State	Published - Jul 17 2017
Event	14th IEEE Annual Consumer Communications and Networking Conference, CCNC 2017 - Las Vegas, United States Duration: Jan 8 2017 → Jan 11 2017

Publication series

Name	2017 14th IEEE Annual Consumer Communications and Networking Conference, CCNC 2017

Conference

Conference	14th IEEE Annual Consumer Communications and Networking Conference, CCNC 2017
Country/Territory	United States
City	Las Vegas
Period	01/8/17 → 01/11/17

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

Keywords

Case-based reasoning Software-defined radio (SDR)
Cognitive engine
Decision-trees

ASJC Scopus subject areas

Computer Networks and Communications
Computer Science Applications
Hardware and Architecture
Communication

Access to Document

10.1109/CCNC.2017.7983233

Cite this

Alqerm, I., & Shihada, B. (2017). Hybrid cognitive engine for radio systems adaptation. In 2017 14th IEEE Annual Consumer Communications and Networking Conference, CCNC 2017 (pp. 778-783). Article 7983233 (2017 14th IEEE Annual Consumer Communications and Networking Conference, CCNC 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CCNC.2017.7983233

@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",

}

Alqerm, I & Shihada, B 2017, Hybrid cognitive engine for radio systems adaptation. in 2017 14th IEEE Annual Consumer Communications and Networking Conference, CCNC 2017., 7983233, 2017 14th IEEE Annual Consumer Communications and Networking Conference, CCNC 2017, Institute of Electrical and Electronics Engineers Inc., pp. 778-783, 14th IEEE Annual Consumer Communications and Networking Conference, CCNC 2017, Las Vegas, United States, 01/8/17. https://doi.org/10.1109/CCNC.2017.7983233

Hybrid cognitive engine for radio systems adaptation. / Alqerm, Ismail; Shihada, Basem.
2017 14th IEEE Annual Consumer Communications and Networking Conference, CCNC 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 778-783 7983233 (2017 14th IEEE Annual Consumer Communications and Networking Conference, CCNC 2017).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Hybrid cognitive engine for radio systems adaptation

AU - Alqerm, Ismail

AU - Shihada, Basem

PY - 2017/7/17

Y1 - 2017/7/17

N2 - 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.

AB - 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.

KW - Case-based reasoning Software-defined radio (SDR)

KW - Cognitive engine

KW - Decision-trees

UR - http://www.scopus.com/inward/record.url?scp=85027415156&partnerID=8YFLogxK

U2 - 10.1109/CCNC.2017.7983233

DO - 10.1109/CCNC.2017.7983233

M3 - Conference contribution

AN - SCOPUS:85027415156

T3 - 2017 14th IEEE Annual Consumer Communications and Networking Conference, CCNC 2017

SP - 778

EP - 783

BT - 2017 14th IEEE Annual Consumer Communications and Networking Conference, CCNC 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 14th IEEE Annual Consumer Communications and Networking Conference, CCNC 2017

Y2 - 8 January 2017 through 11 January 2017

ER -

Hybrid cognitive engine for radio systems adaptation

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this