融合Stackelberg博弈和联邦学习的多星协作频谱认知方法研究

doi:10.11959/j.issn.1000-436x.2024034

Abstract

Abstract:

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 single-node cognitive method, the designed multi-satellite cooperative spectrum cognitive strategy can obtain a better cognitive performance.Moreover, comparing with the existing model, the model utilized in the designed strategy can effectively achieve 96.69% and 93.32% lower number of parameters and required floating point operations per second, whilst maintaining the performance.

Key words: satellite spectrum cognitive, multi-satellite cooperation, Stackelberg game, federated learning

CLC Number:

TN92

Xiaojin DING, Yehui XU, Wen BAO, Gengxin ZHANG. Study on multi-satellite cooperative spectrum cognitive method integrating Stackelberg game and federated learning[J]. Journal on Communications, 2024, 45(2): 90-105.

Figures/Tables 23

References 22

[1]	YIN L , YANG R N , YANG Y Z ,et al. Beam pointing optimization based downlink interference mitigation technique between NGSO satellite systems[J]. IEEE Wireless Communications Letters, 2021,10(11): 2388-2392.
[2]	CHEN Q , GIAMBENE G , YANG L ,et al. Analysis of inter-satellite link paths for LEO mega-constellation networks[J]. IEEE Transactions on Vehicular Technology, 2021,70(3): 2743-2755.
[3]	KASSING S , BHATTACHERJEE D , áGUAS A B , et al . Exploring the “Internet from space” with Hypatia[C]// Proceedings of the ACM Internet Measurement Conference. New York:ACM Press, 2020: 1-16.
[4]	SU Y T , LIU Y Q , ZHOU Y Q ,et al. Broadband LEO satellite communications:architectures and key technologies[J]. IEEE Wireless Communications, 2019,26(2): 55-61.
[5]	CLARK B , GUNZINGER M . Winning the airwaves:regaining America’s dominance in the electromagnetic spectrum[R]. 2015.
[6]	ZHANG C , JIANG C X , JIN J ,et al. Spectrum sensing and recognition in satellite systems[J]. IEEE Transactions on Vehicular Technology, 2019,68(3): 2502-2516.
[7]	DING X J , NI T , ZOU Y L ,et al. Deep learning for satellites based spectrum sensing systems:a low computational complexity perspective[J]. IEEE Transactions on Vehicular Technology, 2023,72(1): 1366-1371.
[8]	王嘉煜, 陈思宏, 张骞丹 . 浅析一种基于DTP的卫星载波监视流程及方法[J]. 中国设备工程, 2022(12): 3-6.
	WANG J Y , CHEN S H , ZHANG Q D . Analysis of a satellite carrier monitoring process and method based on DTP[J]. China Plant Engineering, 2022(12): 3-6.
[9]	JIA M , LIU X , YIN Z S ,et al. Joint cooperative spectrum sensing and spectrum opportunity for satellite cluster communication networks[J]. Ad Hoc Networks, 2017,58: 231-238.
[10]	WANG Y F , DING X J , HONG T ,et al. Distributed-satellite-clusters-based spectrum sensing with two-stage phase alignment[J]. Sensors, 2022,22(11): 3983.
[11]	黄旭民, 张旸, 余荣 ,等. 基于 Stackelberg 博弈的无人机辅助无线供能物联网能量优化[J]. 通信学报, 2022,43(12): 146-156.
	HUANG X M , ZHANG Y , YU R ,et al. Stackelberg game based energy optimization for unmanned aerial vehicle assisted wireless-powered Internet of things[J]. Journal on Communications, 2022,43(12): 146-156.
[12]	CHEN Z B , XU Y Q , WANG H B ,et al. Federated learning-based cooperative spectrum sensing in cognitive radio[J]. IEEE Communications Letters, 2022,26(2): 330-334.
[13]	SUN W , LIAN S , ZHANG H ,et al. Lightweight digital twin and federated learning with distributed incentive in air-ground 6G networks[J]. IEEE Transactions on Network Science and Engineering, 2023,10(3): 1214-1227.
[14]	YANG Z H , CHEN M Z , SAAD W ,et al. Energy efficient federated learning over wireless communication networks[J]. IEEE Transactions on Wireless Communications, 2021,20(3): 1935-1949.
[15]	ACHTERBERG T . SCIP:solving constraint integer programs[J]. Mathematical Programming Computation, 2009,1(1): 1-41.
[16]	CIBLAT P , GHOGHO M . Blind NLLS carrier frequency-offset estimation for QAM,PSK and PAM modulations:performance at low SNR[J]. IEEE Transactions on Communications, 2006,54(10): 1725-1730.
[17]	AN Z L , ZHANG T Q , SHEN M ,et al. Series-constellation feature based blind modulation recognition for beyond 5G MIMO-OFDM systems with channel fading[J]. IEEE Transactions on Cognitive Communications and Networking, 2022,8(2): 793-811.
[18]	任杰, 高岭, 于佳龙 ,等. 面向边缘设备的高能效深度学习任务调度策略[J]. 计算机学报, 2020,43(3): 440-452.
	REN J , GAO L , YU J L ,et al. Energy-efficient deep learning task scheduling strategy for edge device[J]. Chinese Journal of Computers, 2020,43(3): 440-452.
[19]	CHEN Z B , XU Y Q , WANG H B ,et al. Deep STFT-CNN for spectrum sensing in cognitive radio[J]. IEEE Communications Letters, 2021,25(3): 864-868.
[20]	张帆, 黄赟, 方子茁 ,等. 卷积神经网络的损失最小训练后参数量化方法[J]. 通信学报, 2022,43(4): 114-122.
	ZHANG F , HUANG Y , FANG Z Z ,et al. Lost-minimum post-training parameter quantization method for convolutional neural network[J]. Journal on Communications, 2022,43(4): 114-122.
[21]	LUO B , LI X , WANG S Q ,et al. Cost-effective federated learning in mobile edge networks[J]. IEEE Journal on Selected Areas in Communications, 2021,39(12): 3606-3621.
[22]	YANG T T , NING J H , LAN D P ,et al. Kubeedge wireless for integrated communication and computing services everywhere[J]. IEEE Wireless Communications, 2022,29(2): 140-145.

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层名称	输出大小	池化尺寸	步长	堆叠次数	输出通道数
频谱图像	224× 224	—	—	—	3
Conv1MaxPool	112×11256×56	3×33×3	22	1	24
Stage2	28×2828×28	—	21	11	36
Stage3	14×1414×14	—	21	13	48
Stage4	7×77×7	—	21	11	96
Conv5	7×7	1×1	1	1	190
GlobalPool	1×1	7×7	—	—
FC+Softmax	—	—	—	—	N×1

剪枝率	正确率
0	81.14%
5%	81.09%
10%	80.95%
19%	73.15%
22%	72.51%
25%	70.61%
30%	66.97%

卫星数量	正确率	能效
1	83.44%	0.73%
3	85.26%	0.25%
5	91.47%	0.16%
7	91.86%	0.09%

模型	参数量	FLOPs
ShuffleNetV2	1 256 679	151 687 864
ShuffleNetV2-RCPQ	41 499	10 135 344

模型	频谱感知时间/s	干扰检测时间/s	调制模式时间/s	总时间/s
ShuffleNetV2（结合基	27.228 8	43.374 6	62.167	132.770 4
于Stackelberg的博弈的
分配算法）
ShuffleNetV2-RCPQ	11.194	17.760 6	28.990	57.944 6
（结合基于Stackelberg
的博弈的分配算法）
ShuffleNetV2-RCPQ	10.872	17.152 2	32.742	60.766 2
（平均分配）

Study on multi-satellite cooperative spectrum cognitive method integrating Stackelberg game and federated learning

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Figures/Tables 23

References 22

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评估方式	频谱感知性能（P_d）	干扰检测性能（正确率）	调制模式性能（正确率）
测试	90.63%	83.69%	85.51%
仿真	90.85%	84.81%	86.60%

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