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Radio access technology classification for cognitive radio networks | IEEE Conference Publication | IEEE Xplore

Radio access technology classification for cognitive radio networks


Abstract:

In spectrum bands where spectrum sharing is allowed by national regulators, radio access technology recognition is an important technique for reducing interference and fa...Show More

Abstract:

In spectrum bands where spectrum sharing is allowed by national regulators, radio access technology recognition is an important technique for reducing interference and facilitating cooperation among cognitive radios. Unlicensed users (secondaries) need to be able to differentiate between transmissions of licensed users (primaries) and other unlicensed users. Furthermore, secondaries should only free a band when the licensed primary user starts to transmit. In this regard, secondary users' transmission technology classification will have a vital role for coexistence/cooperation purposes in such shared spectrum bands. For the purpose of this work, a practical testbed made up of software defined radio transceivers and a set of computing units was put together. A classification neural network was trained in a supervised learning method. Testbed results demonstrate the efficiency of the classification in differentiating among different radio access transmissions.
Date of Conference: 08-11 September 2013
Date Added to IEEE Xplore: 25 November 2013
Electronic ISBN:978-1-4673-6235-1

ISSN Information:

Conference Location: London, UK
Citations are not available for this document.

I. Introduction

The huge demand to access information wirelessly and ubiquitously in the past decade in conjunction with the scarcity of radio resources [1] has resulted in intensive research towards the concept cognitive radio networks (CRNs) [2]. Nonetheless, only a small subset of published works have found their way into implementation and standardization. This is due to many factors; among them are the ambiguity of the cognitive radio concept for which many definitions exist, and the lack of complete implementable solutions.

Cites in Papers - |

Cites in Papers - IEEE (1)

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1.
Mingzhe Chen, Ursula Challita, Walid Saad, Changchuan Yin, Mérouane Debbah, "Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial", IEEE Communications Surveys & Tutorials, vol.21, no.4, pp.3039-3071, 2019.

Cites in Papers - Other Publishers (4)

1.
Sotirios K. Goudos, "Cognitive Radio Engine Design for IoT Using Monarch Butterfly Optimization and Fuzzy Decision Making", Towards Cognitive IoT Networks, pp.81, 2020.
2.
Kenta Asakura, Haruhisa Ichikawa, Yuusuke Kawakita, "Normalization of Radio Feature for Online Learning for Identification in Cognitive Radio", SICE Journal of Control, Measurement, and System Integration, vol.12, no.4, pp.134, 2019.
3.
Husam Y. Alzaq, B. Berk Ustundag, "Very-low-SNR cognitive receiver based on wavelet preprocessed signal patterns and neural network", EURASIP Journal on Wireless Communications and Networking, vol.2017, no.1, 2017.
4.
Athanasios Paraskevopoulos, Panagiotis I. Dallas, Katherine Siakavara, Sotirios K. Goudos, "Cognitive Radio Engine Design for IoT Using Real-Coded Biogeography-Based Optimization and Fuzzy Decision Making", Wireless Personal Communications, vol.97, no.2, pp.1813, 2017.

References

References is not available for this document.