University of Oulu

A. Al-Tahmeesschi, K. Umebayashi, H. Iwata, M. López-Benítez and J. Lehtomäki, "Applying Deep Neural Networks for Duty Cycle Estimation," 2020 IEEE Wireless Communications and Networking Conference (WCNC), Seoul, Korea (South), 2020, pp. 1-7, doi: 10.1109/WCNC45663.2020.9120720

Applying deep neural networks for duty cycle estimation

Saved in:
Author: Al-Tahmeesschi, Ahmed1; Umebayashi, Kenta1; Iwata, Hiroki1;
Organizations: 1Graduate School of Engineering, Tokyo University of Agriculture and Technology, Japan
2Dept. of Electrical Engineering and Electronics, University of Liverpool, United Kingdom
3ARIES Research Centre, Antonio de Nebrija University, Spain
4Centre for Wireless Communications, University of Oulu, Finland
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.2 MB)
Persistent link:
Language: English
Published: Institute of Electrical and Electronics Engineers, 2020
Publish Date: 2020-11-12


A pro-active spectrum usage prediction is a key technique in decision making and spectrum selection for dynamic spectrum access systems. This work focuses on the estimation of the duty cycle (DC) metric to reflect spectrum usage. The prediction is formulated as a time-series regression problem. Deep neural networks (DNNs) is selected to obtain accurate predictions of channel usage. Namely, Multilayer perceptron (MLP), Long short term memory (LSTM) and a hybrid model based on convolutional neural network followed by an LSTM (CNN-LSTM) layer are selected. The hyper-parameters selection has been optimised utilising both grid search and multi-stage grid search. Moreover, in many cases, the spectrum usage is measured on a smaller time scale from the actual required one. Hence, down-sampling and averaging is required. Averaging operation results in flattening the data and losing essential features to assist DNN to predict the channel usage. We show what is the minimum required time resolution to have a pro-active prediction system. Then, we propose utilising feature engineering to improve prediction accuracy. All the proposed DNNs approaches are trained on real-life measurements. The experimental evaluation demonstrated a high potential of DNNs to learn from previous spectrum usage and accurately predict the spectrum usage. Moreover, adding input features significantly assists the system to achieve accurate predictions in a pro-active manner.

see all

Series: IEEE Wireless Communications and Networking Conference
ISSN: 1525-3511
ISSN-E: 1558-2612
ISSN-L: 1525-3511
ISBN: 978-1-7281-3106-1
ISBN Print: 978-1-7281-3107-8
Pages: 1 - 7
DOI: 10.1109/WCNC45663.2020.9120720
Host publication: 2020 IEEE Wireless Communications and Networking Conference (WCNC)
Conference: IEEE Wireless Communications and Networking Conference
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This work was supported by the European Commission in the framework of the H2020-EUJ-02-2018 project 5GEnhance (Grant agreement no. 815056) and the Ministry of Internal Affairs and Communications (MIC) Japan. The work of M. López Benítez was supported by British Council under UKIERI DST Thematic Partnerships 2016-17 (ref. DST-198/2017). The work of J. Lehtomäki was supported by the Academy of Finland 6Genesis Flagship (grant no. 318927).
EU Grant Number: (815056) 5G-Enhance - 5G Enhanced Mobile Broadband Access Networks in Crowded Environments
Academy of Finland Grant Number: 318927
Detailed Information: 318927 (Academy of Finland Funding decision)
Copyright information: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.