一种面向复杂场景的无线通信节点智能适变架构

An intelligent adaptative architecture for wireless communication in complex scenarios

针对可能存在对抗的未知通信环境,本文探讨了一种能进行智能适变的通信节点体系架构.该架构包括通信环境理解、通信波形适配和智能节点学习进化3个核心功能,以及支持这些功能的通信计算融合硬件平台.所提出的智能适变架构支持通信环境知识库、通信波形库,以及波形与环境适配知识图谱的不断累积和进化,通过波形在线重构,通信节点既能匹配典型通信场景,又能快速适应未知环境,因而支持智能通信节点的可持续发展.进一步本文梳理了强化学习、在线学习和迁移学习等3种机器学习技术在智能适变无线通信节点中的应用,并以最经典的信道估计过程为代表,给出了机器学习应用于通信环境识别的典型范例.

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.