Research Article
Spectrum Prediction in Cognitive Radio Based on Sequence to Sequence Neural Network
@INPROCEEDINGS{10.1007/978-3-030-36405-2_34, author={Ling Xing and Mingbing Li and Yihe Wan and Qun Wan}, title={Spectrum Prediction in Cognitive Radio Based on Sequence to Sequence Neural Network}, proceedings={Advanced Hybrid Information Processing. Third EAI International Conference, ADHIP 2019, Nanjing, China, September 21--22, 2019, Proceedings, Part II}, proceedings_a={ADHIP PART 2}, year={2019}, month={11}, keywords={Cognitive radio Spectrum prediction Sequence to sequence network model}, doi={10.1007/978-3-030-36405-2_34} }- Ling Xing
Mingbing Li
Yihe Wan
Qun Wan
Year: 2019
Spectrum Prediction in Cognitive Radio Based on Sequence to Sequence Neural Network
ADHIP PART 2
Springer
DOI: 10.1007/978-3-030-36405-2_34
Abstract
Cognitive radio provides the ability to access the spectrum that is not used by primary users in an opportunistic manner, enabling dynamic spectrum access technology and improving spectrum utilization. The spectrum prediction plays an important role in key technologies such as spectrum sensing, spectrum decision, spectrum sharing and spectrum mobility in cognitive radio. In this paper, aiming at the spectrum prediction problem in cognitive radio, a spectrum prediction technique based on the sequence to sequence (seq-to-seq) network model constructed by the GRU basic network module is proposed. Due to the long and short time memory function of the GRU network structure, its performance is better than the previous Multi-Layer Perception (MLP) network model. This paper also explores in depth the impact of changes in the length of the input sequence on the prediction results. And the proposed seq-to-seq network model also performs well for multi-slot prediction and multi-channel joint prediction.
