基于二维异步同相正交直方图的调制方式识别OA北大核心CSTPCD

Automatic modulation recognition technology accurately identifies the modulation type of signals,making it a key technology in the field of signal processing.Traditional recognition methods suffer from low accuracy at low signal-to-noise ratios,and performance degradation or failure when dealing with signal frequency instability or asynchronous sampling.In this paper,we investigate modulation recognition technology based on deep learning for low-speed asynchronous sampled signals under channel conditions with varying signal-to-noise ratios and delays.We start by modeling low-speed asynchronous sampled signals and generating a two-dimensional asynchronous in-phase quadrature histogram using their in-phase and quadrature components.Subsequently,we employ a Radial Basis Function Neural Network to extract feature parameters from this two-dimensional image,thus achieving modulation type recognition for the input signal.Extensive computer simulations validate the proposed method's accuracy in recognizing seven modulation types under the influence of additive white Gaussian noise.Experimental results demonstrate that,in the presence of additive white Gaussian noise in the channel model and with an input signal-to-noise ratio of 6 dB,the average recognition accuracy can exceed 95% .Comparative experiments further verify the effectiveness and robustness of the proposed approach.