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Wireless spectrum prediction model based on time series analysis method

Published: 21 September 2009 Publication History

Abstract

Cognitive Radio (CR) is considered to be a solution for mitigating the conflict between limited spectrum resources and increasing demand of various applications. For the purpose of guaranteeing primary users, we propose a spectrum prediction model using the spectrum historic utilization information with time series analysis. We conduct experiment on wide TV bands of China from 603.25 MHz to 843.25 MHz, the results show that this model can accurately predict status of channels and find vacant spectrum for cognitive users. We take channel 2 whose center frequency locates in 615.25 MHz for example, average forecasting error ratio is 1.01% with a minimum of 0.79%.

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Cited By

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  • (2025)Enhanced Multi-Band Spectrum Prediction Using Singular Spectrum Analysis and Attention-Based BiLSTMIEEE Transactions on Cognitive Communications and Networking10.1109/TCCN.2024.341562711:1(118-126)Online publication date: Feb-2025
  • (2025)CL-MFGCN: Graph Structure Contrastive Learning and Multiscale Feature Fusion Graph Convolutional Network for Spectrum PredictionIEEE Internet of Things Journal10.1109/JIOT.2024.346871712:4(3600-3612)Online publication date: 15-Feb-2025
  • (2024)Improved Spectrum Prediction Model for Cognitive Radio Networks Using Hybrid Deep Learning TechniqueInternational Journal of Intelligent Networks10.1016/j.ijin.2024.05.003Online publication date: May-2024
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cover image ACM Conferences
CoRoNet '09: Proceedings of the 2009 ACM workshop on Cognitive radio networks
September 2009
100 pages
ISBN:9781605587387
DOI:10.1145/1614235
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 21 September 2009

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Cited By

View all
  • (2025)Enhanced Multi-Band Spectrum Prediction Using Singular Spectrum Analysis and Attention-Based BiLSTMIEEE Transactions on Cognitive Communications and Networking10.1109/TCCN.2024.341562711:1(118-126)Online publication date: Feb-2025
  • (2025)CL-MFGCN: Graph Structure Contrastive Learning and Multiscale Feature Fusion Graph Convolutional Network for Spectrum PredictionIEEE Internet of Things Journal10.1109/JIOT.2024.346871712:4(3600-3612)Online publication date: 15-Feb-2025
  • (2024)Improved Spectrum Prediction Model for Cognitive Radio Networks Using Hybrid Deep Learning TechniqueInternational Journal of Intelligent Networks10.1016/j.ijin.2024.05.003Online publication date: May-2024
  • (2023)Joint Multidimensional Pattern for Spectrum Prediction Using GNNSensors10.3390/s2321888323:21(8883)Online publication date: 1-Nov-2023
  • (2023)LSTM-enabled prediction-based channel switching scheduling for multi-channel cognitive radio networksPhysical Communication10.1016/j.phycom.2023.10213660(102136)Online publication date: Oct-2023
  • (2020)Experimental Results on the Impact of Memory in Neural Networks for Spectrum Prediction in Land Mobile Radio BandsIEEE Transactions on Cognitive Communications and Networking10.1109/TCCN.2019.29586396:2(771-782)Online publication date: Jun-2020
  • (2019)Spectrum Entropy Prediction Assisted Channel Selection for Secondary UsersIEEE Network10.1109/MNET.2018.180023733:3(190-195)Online publication date: May-2019
  • (2018)Spectrum Inference in Cognitive Radio Networks: Algorithms and ApplicationsIEEE Communications Surveys & Tutorials10.1109/COMST.2017.275105820:1(150-182)Online publication date: Sep-2019
  • (2014)Wireless Spectrum Occupancy Prediction Based on Partial Periodic Pattern MiningIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2013.28325:7(1925-1934)Online publication date: 1-Jul-2014
  • (2013)Channel State Prediction in Cognitive RadioCognitive Radio and Interference Management10.4018/978-1-4666-2005-6.ch015(273-288)Online publication date: 2013
  • Show More Cited By

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