融合Stackelberg博弈和联邦学习的多星协作频谱认知方法研究

TN92; 针对低成本和小型化低轨卫星的监测角度和方向分辨率相对较低、处理能力和峰值功率受限等因素造成单颗低轨卫星频谱认知能力弱的问题,提出了融合Stackelberg博弈和联邦学习的多星协作频谱认知方法.首先,结合各频谱认知卫星的可用算力、认知性能、处理与传输时延等特性,建立面向多频谱认知任务的协作卫星选择与算力资源分配算法;其次,基于所选择的节点和所分配的算力,设计低复杂度的多星协作频谱认知策略,其可自动辨识频谱空洞、检测干扰和识别调制模式.仿真实验结果表明,相比于单节点认知方法,所设计多星协作频谱认知策略能显著提升认知性能,且相比于对比模型,所设计策略中的模型可在不损失性能时,模型的参数...

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Published in通信学报 Vol. 45; no. 2; pp. 90 - 105
Main Authors 丁晓进, 徐叶辉, 包文, 张更新
Format Journal Article
LanguageChinese
Published 南京邮电大学通信与信息工程学院,江苏 南京 210003%南京邮电大学物联网学院,江苏 南京 210003 25.02.2024
Subjects
Stackelberg博弈
Stackelberg game
多星协作
卫星频谱认知
联邦学习
satellite spectrum cognitive
multi-satellite cooperation
federated learning
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Abstract TN92; 针对低成本和小型化低轨卫星的监测角度和方向分辨率相对较低、处理能力和峰值功率受限等因素造成单颗低轨卫星频谱认知能力弱的问题,提出了融合Stackelberg博弈和联邦学习的多星协作频谱认知方法.首先,结合各频谱认知卫星的可用算力、认知性能、处理与传输时延等特性,建立面向多频谱认知任务的协作卫星选择与算力资源分配算法;其次,基于所选择的节点和所分配的算力,设计低复杂度的多星协作频谱认知策略,其可自动辨识频谱空洞、检测干扰和识别调制模式.仿真实验结果表明,相比于单节点认知方法,所设计多星协作频谱认知策略能显著提升认知性能,且相比于对比模型,所设计策略中的模型可在不损失性能时,模型的参数量和浮点运算量降低分别可达96.69%和93.32%.
AbstractList TN92; 针对低成本和小型化低轨卫星的监测角度和方向分辨率相对较低、处理能力和峰值功率受限等因素造成单颗低轨卫星频谱认知能力弱的问题,提出了融合Stackelberg博弈和联邦学习的多星协作频谱认知方法.首先,结合各频谱认知卫星的可用算力、认知性能、处理与传输时延等特性,建立面向多频谱认知任务的协作卫星选择与算力资源分配算法;其次,基于所选择的节点和所分配的算力,设计低复杂度的多星协作频谱认知策略,其可自动辨识频谱空洞、检测干扰和识别调制模式.仿真实验结果表明,相比于单节点认知方法,所设计多星协作频谱认知策略能显著提升认知性能,且相比于对比模型,所设计策略中的模型可在不损失性能时,模型的参数量和浮点运算量降低分别可达96.69%和93.32%.
Abstract_FL To solve the problem of the weak spectrum-cognitive ability caused by monitoring angle,direction resolution,limited processing ability and peak power for a low-earth-orbit(LEO)satellite,a multi-satellite cooperative spectrum cognitive method integrating Stackelberg game and federated learning was proposed.Firstly,considering the available computing resource,cognitive performance,processing and transmission delay of each spectrum cognitive satellite,a cooperative-satellite selection and computing-resource allocation algorithm was built for multiple spectrum-cognitive tasks.Secondly,based on the selected satellites and the allocated computing resources,a low-complexity multi-satellite cooperative spectrum cognitive strategy was further designed,which could automatically sense the spectrum holes,and detect interference as well as identify the modulation mode.Simulation results demonstrate that compared to the sin-gle-node cognitive method,the designed multi-satellite cooperative spectrum cognitive strategy can obtain a better cogni-tive performance.Moreover,comparing with the existing model,the model utilized in the designed strategy can effec-tively achieve 96.69%and 93.32%lower number of parameters and required floating point operations per second,whilst maintaining the performance.
Author 丁晓进
包文
徐叶辉
张更新
AuthorAffiliation 南京邮电大学通信与信息工程学院,江苏 南京 210003%南京邮电大学物联网学院,江苏 南京 210003
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Author_FL ZHANG Gengxin
XU Yehui
DING Xiaojin
BAO Wen
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Keywords Stackelberg博弈
Stackelberg game
多星协作
卫星频谱认知
联邦学习
satellite spectrum cognitive
multi-satellite cooperation
federated learning
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Publisher 南京邮电大学通信与信息工程学院,江苏 南京 210003%南京邮电大学物联网学院,江苏 南京 210003
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Title 融合Stackelberg博弈和联邦学习的多星协作频谱认知方法研究
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