基于AI的端到端语义编码传输方案设计OA

In response to the diverse scenarios of 6G,which demand high performance and low complexity in transmission,this paper proposes an AI-based end-to-end semantic encoding transmission scheme by integrating AI architectures into the joint optimization of wireless transmission protocols.This approach transcends the limitations of traditional modular physical layer design methodologies and classical information theory.Initially,to address the challenge of training transmitters via backpropagation under unknown fading channels,a two-subnetwork architecture based on conditional generative adversarial networks(CGANs)is designed with a phased training methodology,effectively mitigating the effects of fading channels.Furthermore,a system architecture for joint semantic channel coding is introduced,which shows significant advantages in end-to-end joint optimization.Simulation results demonstrate that the proposed scheme enhances system performance through the joint optimization of semantic encoding and transmission,proving its adaptability,intelligence,and efficiency in practical communication settings where channel conditions are unknown.