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cross-band spectrum prediction Algorithm Based on Data Conversion Using Generative Adversarial Networks

中国通信:英文版 2023年第10期20卷 136-152页

作者： Chuang Peng Rangang Zhu Mengbo Zhang Lunwen Wang College of Electronic Engineering National University of Defense TechnologyHefei 230037China

spectrum prediction is one of the new techniques in cognitive radio that predicts changes in the spectrum state and plays a crucial role in improving spectrum sensing performance.prediction models previously trained in the source band tend to perform poorly in the new target band because of changes in the channel.In addition,cognitive radio devices require dynamic spectrum access,which means that the time to retrain the model in the new band is minimal.To increase the amount of data in the target band,we use the GAN to convert the data of source band into target band.First,we analyze the data differences between bands and calculate FID scores to identify the available bands with the slightest difference from the target predicted band.The original GAN structure is unsuitable for converting spectrum data,and we propose the spectrum data conversion GAN(SDC-GAN).The generator module consists of a convolutional network and an LSTM module that can integrate multiple features of the data and can convert data from the source band to the target band.Finally,we use the generated target band data to train the prediction model.The experimental results validate the effectiveness of the proposed algorithm.

关键词： cognitive radio cross-band spectrum prediction deep learning generative adversarial network

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cross-band spectrum prediction Based on Deep Transfer Learning

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China Communications 2020年第2期17卷 66-80页

作者： Fandi Lin Jin Chen Jiachen Sun Guoru Ding Ling Yu College of Communications Engineering Army Engineering University

spectrum prediction is a promising technology to infer future spectrum state by exploiting inherent patterns of historical spectrum data.In practice,for a given spectrum band of interest,when facing relatively scarce historical data,spectrum prediction based on traditional learning methods does not work well.Thus,this paper proposes a cross-band spectrum prediction model based on transfer learning.Firstly,by analysing service activities and computing the distances between various frequency points based on Dynamic Time Warping,the similarity between spectrum bands has been verified.Next,the features,which mainly affect the performance of transfer learning in the crossband spectrum prediction,are explored by leveraging transfer component analysis.Then,the effectiveness of transfer learning for the cross-band spectrum prediction has been demonstrated.Further,experimental results with real-world spectrum data demonstrate that the performance of the proposed model is better than the state-of-theart models when the historical spectrum data is limited.

关键词： cross-band spectrum prediction deep transfer learning long short-term memory dynamic time warping transfer component analysis

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spectrum prediction Based on GAN and Deep Transfer Learning:A cross-band Data Augmentation Framework

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China Communications 2021年第1期18卷 18-32页

作者： Fandi Lin Jin Chen Guoru Ding Yutao Jiao Jiachen Sun Haichao Wang College of Communications Engineering Army Engineering UniversityNanjing 210007China

This paper investigates the problem of data scarcity in spectrum prediction.A cognitive radio equipment may frequently switch the target frequency as the electromagnetic environment changes.The previously trained model for prediction often cannot maintain a good performance when facing small amount of historical data of the new target frequency.Moreover,the cognitive radio equipment usually implements the dynamic spectrum access in real time which means the time to recollect the data of the new task frequency band and retrain the model is very limited.To address the above issues,we develop a crossband data augmentation framework for spectrum prediction by leveraging the recent advances of generative adversarial network(GAN)and deep transfer learning.Firstly,through the similarity measurement,we pre-train a GAN model using the historical data of the frequency band that is the most similar to the target frequency band.Then,through the data augmentation by feeding the small amount of the target data into the pre-trained GAN,temporal-spectral residual network is further trained using deep transfer learning and the generated data with high similarity from GAN.Finally,experiment results demonstrate the effectiveness of the proposed framework.

关键词： cognitive radio cross-band spectrum prediction deep transfer learning generative adversarial network cross-band data augmentation framework

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