基于深度学习的无线通信频谱感知优化方法

Optimization Method for Wireless Communication Spectrum Perception Based on Deep Learning

随着无线通信技术的快速发展,频谱资源的有效利用成为提升网络性能的关键问题。提出一种基于长短时记忆(Long Short Term Memory,LSTM)网络的无线通信频谱感知优化方法,通过建模分析时序频谱数据,实现动态无线环境的高效感知。具体介绍了LSTM网络的结构,深入研究基于LSTM的频谱感知方法,将LSTM的输出与通信系统的参数关联,优化网络性能。分析表明,该方法对提高频谱感知的精度有重要作用,使通信系统能够实时适应不断变化的频谱条件,进而有效提升网络性能。

With the rapid development of wireless communication technology,the effective utilization of spectrum resources has become a key issue to enhance network performance.A spectrum sensing optimization method based on Long Short Term Memory(LSTM)network is proposed to achieve efficient sensing of dynamic wireless environment by modeling and analyzing time-series spectrum data.The structure of the LSTM network is specifically introduced,and the LSTM-based spectrum sensing method is studied in depth to optimize the network performance by correlating the output of the LSTM with the parameters of the communication system.The analysis shows that the method plays an important role in improving the accuracy of spectrum sensing,so that the communication system can adapt to the changing spectrum conditions in real time,and then effectively improve the network performance.