Spectrum Cost Optimization for Cognitive Radio Transmission over TV White Spaces Using Artificial Neural Networks

Ozturk, M., Abubakar, A. I. , Hassan, N. U., Hussain, S. , Imran, M. A. and Yuen, C. (2019) Spectrum Cost Optimization for Cognitive Radio Transmission over TV White Spaces Using Artificial Neural Networks. In: 4th International Conference on UK - China Emerging Technologies (UCET 2019), Glasgow, UK, 21-22 Aug 2019, ISBN 9781728127972 (doi: 10.1109/UCET.2019.8881893)

191550.pdf - Accepted Version



In this paper, the use of TV White Spaces (TVWS) by small cognitive radio wireless network operators (SCWNOs) is considered in order to support the growing demands for IoT applications in smart grid and smart cities. In order to support the wide range of services and applications that are being offered by SCWNOS, spectrum leasing could be considered as an alternative solution to achieve improved Quality of Service (QoS). We consider a situation whereby in order to satisfy the QoS requirements, SCWNOs can decide to lease a certain part of the TVWS spectrum that is referred to as high priority TVWS channel (HPC) for a certain period and pay a fee depending on the duration of HPC spectrum usage. We develop an Artificial Neural Networks (ANN) based online algorithm to determine the optimal transmission decision per time slot that would minimise the overall HPC leasing cost of the SCWNOs while satisfying the QoS constraints. The simulations results shows that our proposed ANN based online algorithms outperforms the Lyapunov based online algorithm while its performance is very close to the optimal offline solution with 99% accuracy.

Item Type:Conference Proceedings
Glasgow Author(s) Enlighten ID:Ozturk, Mr Metin and Abubakar, Mr Attai and Imran, Professor Muhammad and Hussain, Dr Sajjad
Authors: Ozturk, M., Abubakar, A. I., Hassan, N. U., Hussain, S., Imran, M. A., and Yuen, C.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
Published Online:24 October 2019
Copyright Holders:Copyright © 2019 IEEE
First Published:First published in 2019 UK/ China Emerging Technologies (UCET)
Publisher Policy:Reproduced in accordance with the publisher copyright policy
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