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An Improved Algorithm Based on First-order Cyclostationarity for Recognition of M-FSK Signals

机译:一种基于一阶循环平稳性的改进算法,用于M-FSK信号识别

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This paper proposes an improved algorithm based on Piecewise Auto Correlation Accumulation (PACA) and first-order cyclostationarity for recognition of M-ary frequency shift keying (M-FSK, M = 2/4/8) signals in communication systems. Theoretical analysis indicates that the number of first-order Cycle Frequencies (CFs) is equal to the modulation order of M-FSK signals. Therefore, by estimating the first-order cyclic moment of the autocorrelation of M-FSK signals and detecting the number of CFs, the modulation order of M-FSK signals can be identified. Based on its multicarrier characteristics, the proposed algorithm does not require a priori information and is not significantly influenced by factors such as timing delay and frequency offset. Further, PACA suppresses noise, and thereby helps to improve the antinoise performance of the algorithm's modulation recognition. The results of simulations conducted confirm the efficacy of the proposed algorithm.
机译:提出了一种基于分段自相关累积(PACA)和一阶循环平稳性的改进算法,用于通信系统中的M进制频移键控(M-FSK,M = 2/4/8)信号的识别。理论分析表明,一阶循环频率(CFs)的数量等于M-FSK信号的调制阶数。因此,通过估计M-FSK信号的自相关的一阶循环矩并检测CF的数量,可以识别M-FSK信号的调制阶数。基于其多载波特性,该算法不需要先验信息,并且不受时序延迟和频率偏移等因素的明显影响。此外,PACA可抑制噪声,从而有助于改善算法的调制识别的抗噪性能。进行的仿真结果证实了该算法的有效性。

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