---
language: en
license: mit
tags:
- signal-processing
- time-series
- telecommunications
- carrier-analysis
---

# 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:
$$L = -\sum_{c=1}^{M} y_{o,c} \log(p_{o,c})$$

## 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.