基于深度学习的传感云sink节点最优能效SWIPT波束成形设计

doi:10.11959/j.issn.1000-436x.2021131

Abstract

Abstract:

To solve the problems of high complexity and poor real-time performance caused by traditional wireless resource management based on optimization methods, the energy efficiency maximization problem of sink node and its mathematical model were established for SWIPT-enabled sensor-cloud system, then the deep learning method was introduced to realize the solving and online decision-making with lower complexity and higher real-time performance.The mathematical model was transformed into a high-dimensional solvable form, and then a SWIFT-WMMSE algorithm with iterated forms was designed to solve optimal beamforming vector.The convergence of SWIPT-WMMSE algorithm was proved.Then, based on error propagation of DNN approximation, the scale design criteria for the DNN was deduced, and the approximation was realized through DNN training.Finally, the simulation results verify the effectiveness of SWIPT-WMMSE and DNN algorithm, as well as the approximation effect of DNN and its system performance gains.

Key words: deep learning, SWIPT, sink node, energy efficiency, beamforming, deep natural network

CLC Number:

TN92

Zhe WANG, Taoshen LI, Lina GE, Guifen ZHANG, Min WU. Optimal energy-efficiency beamforming design for SWIPT-enabled sink in sensor cloud based on deep learning[J]. Journal on Communications, 2021, 42(7): 176-188.

Figures/Tables 10

References 37

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参数	值
小区内传感器节点数K	10
sink节点天线数M_i	5
sink节点发送功率P_i/W	50
数据生成样本总数	30 000
DNN训练样本数	25 000
DNN测试样本数	5 000
批大小bs	10, 50, 100, 500, 1 000
学习率lr	0.01, 0.05, 0.1, 0.5

模型参数	训练阶段参数	最大系统能效/(bit.(Hz.J)^-1)		拟合度：DNN/SWIPT-WMMSE			总CPU用时/s
模型参数	训练阶段参数	DNN	SWIPT-WMMSE	最高	最低	平均	DNN	SWIPT-WMMSE
K=10，M_i=5		7 745	7 801	99.28%	86.09%	94.77%	7.39	255.90
K=20，M_i=5	lr=0.1，bs=10	7 464	7 857	99.00%	82.45%	94.60%	7.62	617.38
K=50，M_i=5		6 852	7 135	98.04%	72.97%	89.80%	8.15	1004.94
K=10，M_i=2	lr=0.1，bs=10	7 532	7 873	98.67%	88.29%	95.51%	7.12	237.22
K=10，M_i=8		7 121	7 516	98.74%	87.60%	92.88%	7.47	319.92
K=10，M_i=5	lr=0.01，bs=10	7 555	7 904	98.58%	73.31%	79.89%	7.41	268.34
	lr=0.05，bs=10	7 460	7 762	99.11%	79.13%	86.79%	7.27	257.92
K=10，M_i=5	lr=0.1，bs=100	7 184	7 283	98.64%	67.70%	86.32%	6.53	248.83
	lr=0.1，bs=1 000	6 348	6 850	92.67%	52.40%	80.91%	6.08	274.74

隐藏层数量		准确度（DNN/SWIPT-WMMSE）			CPU用时（DNN）/s
层	神经元	最高	最低	平均	CPU用时（DNN）/s
3	(1 000,100,10)	99.28%	86.09%	94.77%	7.39
3	(2 000,500,500)	73.23%	7.76%	28.75%	8.89
3	(2 000,1 000,1 000)	71.29%	7.57%	31.75%	10.39
5	(1 000,1 000,500,100,100)	55.37%	3.68%	26.65%	13.83
10	(1 000,1 000,800,800,500, 500,500,300,300,100)	51.17%	4.48%	26.98%	17.35

Optimal energy-efficiency beamforming design for SWIPT-enabled sink in sensor cloud based on deep learning

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