基于循环神经网络的卫星幅相信号调制识别与解调算法

针对卫星通信中常用的幅相调制信号，提出了一种基于循环神经网络的信号识别与解调模型.通过循环神经单元直接对信号时序进行深层特征提取，结合全连接神经网络对特征进行维度映射，最终完成目标信号的调制识别与解调.该方法不需要预估目标信号载噪比，克服了人为确定阈值的缺陷，对信号频偏误差、定时误差容忍能力强；且在开发维护和更新拓展方面，克服了传统算法需重新部署判决规则的缺点，符合实际工程需求.仿真实验表明，当网络训练达到稳态时，在信噪比为6dB的条件下，目标信号识别率接近98%，解调误码率接近理论门限.本文所建立的理论形式为当今智能化信号处理提供了新思路，其思想同样可应用于其他通信信号处理领域.

Satellite Amplitude-Phase Signals Modulation Identification and Demodulation Algorithm Based on the Cyclic Neural Network

A cognitive signal recognition and demodulation model is designed based on the cyclic neural network for conventional amplitude-phase satellite modulations.Through the cyclic neural unit,the features of the target signal are extracted.And the features are dimension-mapped by the fully connected neural network.The model finally completes the modulation recognition and demodulation of the target signal with these mapped features.This method does not need much prior knowledge about signal-to-noise ratio (S/N),and it is not sensitive to frequency offset.The method also has good adaptability in the maintenance and extension,which conforms to the demands of the engineering,while the traditional algorithms need to redeploy the decision rule.Computer simulations show that the correct recognition probability is close to 98% when S/N is greater than 6 dB and demodulation error rate is close to the theoretical gate.The presented theoretical form provides a new idea for intelligent signal processing,and it can also be used in other communication signal processing fields.