This simulation model demonstrates semantic-aware transmission in wireless networks using a deep learning architecture. The system employs jointly trained CNN-based transmitters and receivers to achieve end-to-end optimization of semantic communication.
- Utilises multiple CNN layers for semantic feature extraction.
- Implements Generalized Divisive Normalization (GDN) after convolutional layers.
- Processes input data to preserve essential semantic information while removing redundancy.
- Final layers map extracted features to channel symbols for efficient transmission.
- Simulates AWGN and Rayleigh fading channels.
- Incorporates path loss and multi-path effects.
- Features adjustable SNR levels and interference patterns to test system performance.
- Utilises CNN layers for signal processing.
- Focuses on preserving semantic meaning rather than exact signal reproduction.
- Achieves more efficient communication compared to traditional bit-level accuracy systems.
- Trained using the CIFAR-10 dataset.
- End-to-end optimization for balancing semantic accuracy and reconstruction quality.
- The loss function ensures semantic fidelity while adapting to channel conditions.
- PSNR Comparison: The received image quality is evaluated under deep learning-based wireless communication (joint source-channel coding) versus traditional models (LDPC for channel coding, BPG for source coding) at different SNR levels.
- Image Classification Accuracy: The system's ability to classify images at the destination is measured under different SNR conditions.