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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2601.13205 |
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| _version_ | 1866909995286659072 |
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| author | Tortayev, Kadyrzhan Damborg, Oliver Falkenberg Joensen, Jònas À Hàlvmørk Pedesk, Jonas Li, Yifa Zhang, Fengchun An, Zeliang Wang, Yubo Shen, Ming |
| author_facet | Tortayev, Kadyrzhan Damborg, Oliver Falkenberg Joensen, Jònas À Hàlvmørk Pedesk, Jonas Li, Yifa Zhang, Fengchun An, Zeliang Wang, Yubo Shen, Ming |
| contents | Unmanned aerial vehicles (UAVs) enable efficient in-situ radiation characterization of large-aperture antennas directly in their deployment environments. In such measurements, a continuous-wave (CW) probe tone is commonly transmitted to characterize the antenna response. However, active co-channel emissions from neighboring antennas often introduce severe in-band interference, where classical FFT-based estimators fail to accurately estimate the CW tone amplitude when the signal-to-interference ratios (SIR) falls below -10 dB. This paper proposes a lightweight deep convolutional neural network (DC-CNN) that estimates the amplitude of the CW tone. The model is trained and evaluated on real 5~GHz measurement bursts spanning an effective SIR range of --33.3 dB to +46.7 dB. Despite its compact size (<20k parameters), the proposed DC-CNN achieves a mean absolute error (MAE) of 7% over the full range, with <1 dB error for SIR >= -30 dB. This robustness and efficiency make DC-CNN suitable for deployment on embedded UAV platforms for interference-resilient antenna pattern characterization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_13205 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Co-Channel Interference Mitigation Using Deep Learning for Drone-Based Large-Scale Antenna Measurements Tortayev, Kadyrzhan Damborg, Oliver Falkenberg Joensen, Jònas À Hàlvmørk Pedesk, Jonas Li, Yifa Zhang, Fengchun An, Zeliang Wang, Yubo Shen, Ming Signal Processing Unmanned aerial vehicles (UAVs) enable efficient in-situ radiation characterization of large-aperture antennas directly in their deployment environments. In such measurements, a continuous-wave (CW) probe tone is commonly transmitted to characterize the antenna response. However, active co-channel emissions from neighboring antennas often introduce severe in-band interference, where classical FFT-based estimators fail to accurately estimate the CW tone amplitude when the signal-to-interference ratios (SIR) falls below -10 dB. This paper proposes a lightweight deep convolutional neural network (DC-CNN) that estimates the amplitude of the CW tone. The model is trained and evaluated on real 5~GHz measurement bursts spanning an effective SIR range of --33.3 dB to +46.7 dB. Despite its compact size (<20k parameters), the proposed DC-CNN achieves a mean absolute error (MAE) of 7% over the full range, with <1 dB error for SIR >= -30 dB. This robustness and efficiency make DC-CNN suitable for deployment on embedded UAV platforms for interference-resilient antenna pattern characterization. |
| title | Co-Channel Interference Mitigation Using Deep Learning for Drone-Based Large-Scale Antenna Measurements |
| topic | Signal Processing |
| url | https://arxiv.org/abs/2601.13205 |