Source Channel Coding

Source channel coding aims to efficiently and reliably transmit data by jointly optimizing source compression and channel coding, unlike traditional separate approaches. Current research heavily utilizes deep learning, employing neural networks (like variational autoencoders and generative adversarial networks) within joint source-channel coding (JSCC) frameworks to achieve better rate-distortion performance and enhance perceptual quality, often incorporating semantic information for improved robustness and efficiency. This approach shows promise for improving data transmission across diverse and challenging communication scenarios, particularly in applications like cross-technology communication and semantic communication where preserving meaning is paramount. The focus is on optimizing for perceptual quality metrics alongside traditional distortion measures, leading to more efficient and human-perceptually satisfying communication systems.