面向6G的内生智能频谱管控关键技术研究OA
Survey on key technologies for native-intelligence-driven spectrum management towards 6G
随着通信网络的不断演进,用频设备将持续增长,导致频谱资源严重稀缺.6G内生智能通过机器学习等技术实现频谱管控,能有效提升频谱利用率.基于此,综述了面向6G的内生智能频谱管控关键技术.首先,分析了6G的频谱演进趋势.其次,构建了一种三层频谱管控分析框架,并基于该框架对比分析了6G内生智能方法在协作频谱感知与动态频谱接入中的决策应用,总结了基于区块链及机器学习的频谱管控安全保障方案.最后,讨论了潜在挑战与未来展望.
The evolution of communication networks is intensifying spectrum scarcity due to the growing number of frequency-dependent devices.Native-intelligence in 6G,which leverages machine learning for spectrum management,of-fers an effective way to enhance spectrum utilization.The research on key technologies for native-intelligence-driven spectrum management in 6G was surveyed.Firstly,emerging trends in spectrum evolution were analyzed.Secondly,a three-layer spectrum management analysis framework was established.Within this framework,native-intelligence-driven decision-making applications in cooperative spectrum sensing and dynamic spectrum access were compared,and secu-rity solutions based on blockchain and machine learning were summarized.Finally,potential challenges and future re-search directions were discussed.
王先梅;任语铮;姜天宇;张海君;马旭
北京科技大学河北省空天地智能通信重点实验室,北京 100083北京科技大学河北省空天地智能通信重点实验室,北京 100083北京科技大学河北省空天地智能通信重点实验室,北京 100083北京科技大学河北省空天地智能通信重点实验室,北京 100083北京科技大学河北省空天地智能通信重点实验室,北京 100083
信息技术与安全科学
频谱管控协作频谱感知动态频谱接入机器学习区块链
spectrum managementcooperative spectrum sensingdynamic spectrum accessmachine learningblockchain
《通信学报》 2026 (2)
1-19,19
国家自然科学基金资助项目(No.62225103,No.U22B2003,No.U2441227)北京市自然科学基金资助项目(No.L253003,No.L241008) The National Natural Science Foundation of China(No.62225103,No.U22B2003,No.U2441227),Beijing Natu-ral Science Foundation(No.L253003,No.L241008)
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