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--- |
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language: en |
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license: mit |
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tags: |
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- signal-processing |
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- time-series |
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- telecommunications |
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- carrier-analysis |
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--- |
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# Data Signal & Carrier Classifier |
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## Overview |
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This model is designed for high-frequency telecommunications monitoring. It analyzes the **Data Signal** (তথ্য সংকেত) integrity and identifies the underlying **Carrier** (বাহক) modulation type in real-time. By processing raw I/Q samples, it can distinguish between various modulation schemes and detect signal degradation caused by environmental interference. |
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## Model Architecture |
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The model utilizes a **Temporal-Frequency Transformer** architecture: |
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- **Feature Extraction**: A 1D-Convolutional front-end processes the raw waveform to extract local spectral features. |
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- **Contextual Encoder**: 8 layers of Multi-Head Self-Attention capture long-range dependencies across the **Carrier** wave. |
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- **Classification Head**: A linear layer that maps the hidden state of the `[CLS]` token to the specific modulation class. |
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- **Loss Function**: Weighted Cross-Entropy to handle class imbalance in noisy environments: |
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$$L = -\sum_{c=1}^{M} y_{o,c} \log(p_{o,c})$$ |
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## Intended Use |
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- **Spectrum Sensing**: Automated identification of occupied bands in cognitive radio networks. |
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- **Quality of Service (QoS)**: Monitoring the health of a **Data Signal** to trigger automated re-routing. |
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- **Anomaly Detection**: Identifying unauthorized **Carrier** frequencies in restricted zones. |
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## Limitations |
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- **Signal-to-Noise Ratio (SNR)**: Performance degrades significantly when the SNR drops below 3dB. |
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- **Frequency Offset**: Requires external synchronization; high frequency-offset values may lead to misclassification. |
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- **Hardware constraints**: Optimized for FPGA or GPU inference; may require quantization for edge deployment on low-power microcontrollers. |
