脉冲噪声环境下基于循环谱的BPSK调制信号码元速率估计OA

[Objective]In wireless communication systems,signals are not only affected by Gaussian noise,but also interfered by impulse noise,such as electromagnetic interference and lightning noise.This noise is featured with non-Gaussian,abruptness,and strong impulse,which leads to severely degraded performance of traditional signal processing methods based on Gaussian noise.Binary phase shift keying(BPSK),as a traditional basic modulation method,is widely used in satellite communication,underwater acoustic communication,military communication and other fields.Its symbol rate estimation is a key link in signal demodulation,synchronization and parameter blind recognition.However,in the impulse noise environment,traditional symbol rate estimation methods rely on the statistical characteristics of the signal,resulting in severe deterioration or even complete failure of estimation performance.The cyclic spectrum theory can effectively utilize the cyclostationary characteristics of signals to separate signals and noise in the frequency domain and the cyclic frequency two-dimensional plane,providing ideas for parameter estimation in impulse noise environments.However,traditional cyclic spectrum theory methods are susceptible to impulse noise interference during the extraction of cyclic spectral lines,resulting in reduced estimation performance.[Methods]To address this issue,a high-precision symbol rate estimation method was proposed based on an improved cyclic spectrum.This method employed the cyclostationary characteristics of BPSK signals to construct a corresponding nonlinear function,and sequentially calculated the autocorrelation function,cyclic correlation function,and cyclic spectrum function.The impulse noise and white Gaussian noise(WGN)were both zero at non-zero cyclic frequencies in the BPSK signal cyclic spectrum after nonlinear transformation.With these characteristics,the symbol rate of the signal was accurately estimated by searching for spectral peaks and corresponding cyclic frequencies on the Fourier frequency and spectral line plane of the received signal cyclic spectrum.[Results]Nonlinear transformation optimizes the second-order statistics of the signal while keeping the phase information unchanged,thereby reducing the interference of impulse noise.This method shows superior performance in impulse noise environments.The suppression mechanism of the improved cyclic spectrum on α stable distribution is revealed and the effectiveness of discrete spectrum peak detection is verified,providing a highly robust symbol rate estimation scheme for impulse noise environments.[Conclusions]Compared with the wavelet transform method,the proposed method can effectively lower the effect of impulse noise on symbol rate estimation,with higher estimation accuracy and lower algorithm complexity.According to computer simulation results,the proposed method has a normalized mean square error(NMSE)lower than that of the existing methods.As the value of stable distributed α increases,the NMSE decreases.This further demonstrates that the proposed method has higher estimation accuracy,more significant performance advantages at high signal-to-noise ratios(SNR),and better robustness under different impulse noise conditions.The application scenario is broader.