Carrier frequency and symbol rate estimation based on cyclic spectrum

Abstract

Carrier frequency and symbol rate estimation are the main contents of parameter estimation, which is the basis of modulation recognition and further processing of signals especially in non-cooperative communication. With the development of wireless communication, the signal transmission environment has become increasingly bad, causing more difficulties in parameter estimation. It is well known that the signal cycle spectrum is robust to noises and signal parameters are closely related. In practice, it is impossible to calculate the cyclic spectrum of infinite length data signals. When using finite length data to obtain a cycle spectrum, the truncation noise is induced, resulting in interference. It is necessary to overcome the influence of noises in order to improve the detection ability of discrete spectral lines. An improved method of the discrete spectral line extraction algorithm is proposed by reflecting the amplitude advantage of discrete spectral lines through salient features of continuous noises in discrete spectral line neighborhood.

Automatic Modulation Recognition Based on Deep-Learning Features Fusion of Signal and Constellation Diagram

“…(3) Image representation. The signal is transformed into a two-dimensional image, which includes constellation diagrams [30,31], time-frequency maps [32,33], cyclic spectrums [34,35], etc. Actually, the task is transformed into an image problem, and then the classic image recognition algorithm is used for recognition.…”