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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2408.08179 |
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| _version_ | 1866909288056750080 |
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| author | Pourranjbar, Ali Kaddoum, Georges Mba, Verdier Assoume Garg, Sahil Singh, Satinder |
| author_facet | Pourranjbar, Ali Kaddoum, Georges Mba, Verdier Assoume Garg, Sahil Singh, Satinder |
| contents | We propose a blind ML-based modulation detection for OFDM-based technologies. Unlike previous works that assume an ideal environment with precise knowledge of subcarrier count and cyclic prefix location, we consider blind modulation detection while accounting for realistic environmental parameters and imperfections. Our approach employs a ResNet network to simultaneously detect the modulation type and accurately locate the cyclic prefix. Specifically, after eliminating the environmental impact from the signal and accurately extracting the OFDM symbols, we convert these symbols into scatter plots. Due to their unique shapes, these scatter plots are then classified using ResNet. As a result, our proposed modulation classification method can be applied to any OFDM-based technology without prior knowledge of the transmitted signal. We evaluate its performance across various modulation schemes and subcarrier numbers. Simulation results show that our method achieves a modulation detection accuracy exceeding $80\%$ at an SNR of $10$ dB and $95\%$ at an SNR of $25$ dB. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_08179 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Machine learning empowered Modulation detection for OFDM-based signals Pourranjbar, Ali Kaddoum, Georges Mba, Verdier Assoume Garg, Sahil Singh, Satinder Machine Learning We propose a blind ML-based modulation detection for OFDM-based technologies. Unlike previous works that assume an ideal environment with precise knowledge of subcarrier count and cyclic prefix location, we consider blind modulation detection while accounting for realistic environmental parameters and imperfections. Our approach employs a ResNet network to simultaneously detect the modulation type and accurately locate the cyclic prefix. Specifically, after eliminating the environmental impact from the signal and accurately extracting the OFDM symbols, we convert these symbols into scatter plots. Due to their unique shapes, these scatter plots are then classified using ResNet. As a result, our proposed modulation classification method can be applied to any OFDM-based technology without prior knowledge of the transmitted signal. We evaluate its performance across various modulation schemes and subcarrier numbers. Simulation results show that our method achieves a modulation detection accuracy exceeding $80\%$ at an SNR of $10$ dB and $95\%$ at an SNR of $25$ dB. |
| title | Machine learning empowered Modulation detection for OFDM-based signals |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2408.08179 |