Wireless communication systems that incorporate digital twin(DT)alongside artificial intelligence(AI)are expected to transform 6G networks by providing advanced features for predictive modeling and decision making.The...Wireless communication systems that incorporate digital twin(DT)alongside artificial intelligence(AI)are expected to transform 6G networks by providing advanced features for predictive modeling and decision making.The key component is the creation of DT channels,which form the basis for upcoming applications.However,the existing work of channel predictive generation only considers time dimension,distribution-oriented or multi-step slidingwindow prediction schemes,which is not accurate and efficient for real-time DT communication systems.Therefore,we propose the wireless channel generative adversarial network(WCGAN)to tackle the issue of generating authentic long-batch channels for DT applications.The generator based on convolutional neural networks(CNN)extracts features from both the time and frequency domains to better capture the correlation.The loss function is designed to ensure that the generated channels consistently match the physical channels over an extended period while sharing the same probability distributions.Meanwhile,the accumulating error from the slicing window has been alleviated.The simulation demonstrates that an accurate and efficient DT channel can be generated by employing our proposed WCGAN in various scenarios.展开更多
文摘Wireless communication systems that incorporate digital twin(DT)alongside artificial intelligence(AI)are expected to transform 6G networks by providing advanced features for predictive modeling and decision making.The key component is the creation of DT channels,which form the basis for upcoming applications.However,the existing work of channel predictive generation only considers time dimension,distribution-oriented or multi-step slidingwindow prediction schemes,which is not accurate and efficient for real-time DT communication systems.Therefore,we propose the wireless channel generative adversarial network(WCGAN)to tackle the issue of generating authentic long-batch channels for DT applications.The generator based on convolutional neural networks(CNN)extracts features from both the time and frequency domains to better capture the correlation.The loss function is designed to ensure that the generated channels consistently match the physical channels over an extended period while sharing the same probability distributions.Meanwhile,the accumulating error from the slicing window has been alleviated.The simulation demonstrates that an accurate and efficient DT channel can be generated by employing our proposed WCGAN in various scenarios.
