An intelligent adaptative architecture for wireless communication in complex scenarios

This paper discusses a communication node architecture that can intelligently adapt to the unknown and possible confrontation-communication environment. The architecture includes three core functions: communication environment understanding, communication waveform adaptation, and learning and evolution. To support these functions, it also provides a hardware platform that integrates the capabilities of communication and computation. The proposed intelligent adaptive architecture supports the continuous accumulation and evolution of knowledge bases of communication environments, communication waveforms, and the match between them. Through online waveform reconfiguration, communication nodes can adapt to typical communication scenarios and unknown environments, thus support the sustainable development of intelligent communication. Furthermore, this paper summarizes the applications of reinforcement learning, online learning, and transfer learning in intelligent adaptive wireless communications, also providing a typical example of the application of machine learning to the channel estimation process.