基于时频相关性融合的深度网络频谱预测方法

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A spectrumprediction network based on time-frequency correlation fusion mechanism

ZHANG Zongchangl，LI Lin¹，XIE Maolin²，ZANG Bo¹，YU Fei （1.School of Electronic Engineering，Xidian University，Xi'an 7lOO71， Shanxi， China; 2.China Xi'an Satellite Control Center，Xi'an 71OO43，Shanxi，China; 3.Unit 31007 of PLA，Beijing 100000，China）

Abstract： With the development of modern communication technology，the electromagnetic spectrum environment is becoming increasingly complex.Frequency spectrum prediction is an important technology in cognitive radio. It plays a significant role in spectrum resource scheduling and spectrum interference avoidance.Traditional spectrum prediction methods are susceptible to noise and its performance is limited when the amount of spectral data is large. Therefore，a time-frequency correlation capture module as a network branch for correlation feature extraction is designed. A spectrum prediction model based on sequence-to-sequence architecture deep network is also built. The dataset is generated by using software radio equipment to collect electromagnetic environment around the campus.As verified by the measured spectrum data，the method improves significantly in spectrum multi-channel prediction.

Key words：spectrum prediction；cognitive radio；correlation capture;deep network model; meaured spectrum dataset

0 引言

频谱认知与智能管控是现代认知无线电的重要组成部分，频谱预测作为认知无线电的一项关键技术，通过获取频谱感知的历史信息，分析未来频谱的变化态势，从而可以预判性地做出频谱占用策略调整[1]。（剩余7341字）