Automatic Modulation Classification Under Large Frequency Offset: A Robust Approach Integrating Pre-classification and Compensation Mechanism
Open Access
- 23 February 2024
- conference paper
- Published by Association for Computing Machinery (ACM)
- p. 165-171
Abstract
Automatic Modulation Classification (AMC) is a critical technology in the field of non-cooperative communication. Carrier frequency offset is an inevitable impairment factor in communication systems, and it significantly affects the performance of AMC, especially when the range is large. Therefore, frequency offset estimation and compensation are essential for automatic modulation classification. To address this issue, this paper proposes an innovative cascading network based on deep learning, primarily comprising pre-classification and compensation mechanisms. With the pre-classification mechanism, we categorize the eleven unknown modulation formats into three major classes: PSK, FSK, and QAM. Subsequently, we utilize deep neural networks to estimate the frequency offset of signals within these major classes, followed by performing the corresponding signal compensation. The experimental results demonstrate that the pre-classification mechanism effectively reduces the error in frequency offset estimation, minimizing the error (mean-square error) to as low as -3.9. Under various signal-to-noise ratio (SNR) ranges, the classification accuracy achieved through pre-classification and compensation is up to 14% higher than the accuracy without this method. Specifically, at SNR = 10 dB, it can reach 99%. Moreover, the proposed scheme is robust and performs stably across different frequency offsets range, outperforming other existing network classification schemes to some extent.Keywords
This publication has 17 references indexed in Scilit:
- A Data Preprocessing Method for Automatic Modulation Classification Based on CNNIEEE Communications Letters, 2020
- A Comparative Study between CNN, LSTM, and CLDNN Models in The Context of Radio Modulation Classification2020
- Spectrum Analysis and Convolutional Neural Network for Automatic Modulation RecognitionIEEE Wireless Communications Letters, 2019
- Deep Learning Models for Wireless Signal Classification With Distributed Low-Cost Spectrum SensorsIEEE Transactions on Cognitive Communications and Networking, 2018
- Over-the-Air Deep Learning Based Radio Signal ClassificationIEEE Journal of Selected Topics in Signal Processing, 2018
- Cyclostationarity-Based Robust Algorithms for QAM Signal IdentificationIEEE Communications Letters, 2011
- Automatic modulation classification algorithm using higher-order cumulants under real-world channel conditionsIEEE Communications Letters, 2009
- Antenna array likelihood modulation classifier for BPSK and QPSK signals2003
- Digital modulation classification: the BPSK versus QPSK case2003
- Maximum-likelihood classification for digital amplitude-phase modulationsIEEE Transactions on Communications, 2000