Shoriful025/data_signal_carrier_classifier

Data Signal & Carrier Classifier

Overview

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.

Model Architecture

The model utilizes a Temporal-Frequency Transformer architecture:

• Feature Extraction: A 1D-Convolutional front-end processes the raw waveform to extract local spectral features.
• Contextual Encoder: 8 layers of Multi-Head Self-Attention capture long-range dependencies across the Carrier wave.
• Classification Head: A linear layer that maps the hidden state of the [CLS] token to the specific modulation class.
• Loss Function: Weighted Cross-Entropy to handle class imbalance in noisy environments: $$L=-\sum _{c=1}^M{y}_{o,c}\log (p_{o,c})L\; =\; -\backslash sum\_\{c=1\}^\{M\}\; y\_\{o,c\}\; \backslash log(p\_\{o,c\})$$

Intended Use

• Spectrum Sensing: Automated identification of occupied bands in cognitive radio networks.
• Quality of Service (QoS): Monitoring the health of a Data Signal to trigger automated re-routing.
• Anomaly Detection: Identifying unauthorized Carrier frequencies in restricted zones.

Limitations

• Signal-to-Noise Ratio (SNR): Performance degrades significantly when the SNR drops below 3dB.
• Frequency Offset: Requires external synchronization; high frequency-offset values may lead to misclassification.
• Hardware constraints: Optimized for FPGA or GPU inference; may require quantization for edge deployment on low-power microcontrollers.