Abstract
In order to achieve automatic identification of modulation formats in orthogonal frequency division multiplexing (OFDM) subcarrier signals, a recognition method based on multiple feature inputs and a Hybrid Training Neural Network (HTNN) is proposed, in which an HTNN structure is designed to obtain high-order statistical correlation features and constellations of OFDM subcarriers. The recognition performance of the proposed method in free space channel transmission and atmospheric time-varying channel transmission is studied by simulation. Simulation results show that the overall identification accuracy of the recognition model based on the proposed method exceeded 93.37% in the wide Signal-to-Noise Ratio (SNR) range of the free space channel. With an SNR higher than 7.5 dB, identification accuracy performance of the learning model culminated, achieving 100% identification accuracy of OFDM subcarrier signals. Under weak turbulent atmospheric and time-varying channel conditions, the overall identification accuracy curve tended to increase as SNR increased and stabilized at more than 95%. Compared with the two comparison methods, the proposed method reduced the sensitivity to channel variations and improved the convergence speed on the basis of the guaranteed identification accuracy, and enabled reliable identification of OFDM subcarrier signals in a wide SNR range.
Highlights
Publisher’s Note: MDPI stays neutralDue to the increase in space engineering projects and tasks, the complexity of modulation formats in satellite laser communication systems is increasing in order to meet the needs of different users and services and make full use of channel capacity [1–7]
In order to ensure the security and accuracy of information transmission, the receiver needs to obtain the modulation format of the laser signal to demodulate the received signal, and it has become a key procedure in the study of automatic modulation format identification of Optical Frequency Division Multiplexing (OFDM) subcarrier signals
Ranges, with the overall identification accuracy fluctuating around 73%, which indicates that the characteristic parameters of high-order statistics of OFDM signals are not affected by the Signal-to-Noise Ratio (SNR)
Summary
Due to the increase in space engineering projects and tasks, the complexity of modulation formats in satellite laser communication systems is increasing in order to meet the needs of different users and services and make full use of channel capacity [1–7]. Based on neural network algorithms, instantaneous information features, transform domain features, cumulative features, and signal constellation features, researchers proposed a variety of signal modulation format recognition methods to improve recognition performance [18–27]. As the double-input characteristics of the proposed multi-input hybrid training neural network, the HTNN is trained to obtain the high-order correlation features of multi-feature inputs and get the model to realize automatic modulation format recognition among OFDM subcarriers. Through the analysis of the performance under the two conditions of free-space channel transmission and atmospheric time-varying channel transmission, simulation results verified that the proposed method could reduce the sensitivity to channel changes on the basis of ensuring the identification accuracy, improve the convergence speed, and realize the reliability recognition of the OFDM subcarrier signal under a wide range of SNRs
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