基于LSTM的LEO卫星链路自适应算法

doi:10.3969/j.issn.1001-506X.2021.01.29

Abstract

Abstract:

Low earth orbit (LEO) satellite becomes an important part of the space-air-ground integrated network for its low transmission loss and little terrestrial interference. Due to the large delay of satellite-to-ground transmission link, the existing satellite communication process cannot achieve real-time channel state information interaction, which leads to the system's inability to adapt to the change of channel status. To solve this problem, an signal to noise ratio (SNR) prediction method based on long short-term memory (LSTM) network is proposed. Modulation and coding scheme (MCS) can be adjusted based on the predicted SNR to match the rapidly changing channel. Simulation results show that the proposed SNR prediction method based on LSTM network can achieve high accuracy, and the adjustment of MCS based on the predicted SNR in real-time can greatly improve the overall throughput of the system.

Key words: low orbit satellite communication, system throughput, signal to noise ratio (SNR) prediction, long short-term memory (LSTM) network, modulation and coding scheme

CLC Number:

TN927

Xiaoyue HU, Kai KANG, Hua QIAN, Shunqing ZHANG. Link adaptation algorithm based on LSTM network for LEO satellite[J]. Systems Engineering and Electronics, 2021, 43(1): 237-243.

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References 16

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参数	符号	数值
载波频率/GHz	f	29.9
发送功率/dBW	P_t	60
符号速率/Msps	R_s	20
发射机天线增益/dB	G_t	5
接收天线增益/dB	G_r	5
带宽/MHz	B	24
卫星轨道高度/km	H_s	550、765
地面站经度/纬度/(°)	δ_lon/δ_lat	121/31

卫星总吞吐量	CCM/Gbit	VCM/Gbit	性能提升/%
轨道1卫星总吞吐量	43.0	54.2	26.0
轨道2卫星总吞吐量	43.4	59.6	37.3

μ_m	μ_c	切换门限值	频谱效率
2(QPSK)	2/3	3.1	0.66
	3/4	4.03	0.75
	4/5	4.68	0.80
	5/6	5.18	0.83
3(8PSK)	2/3	6.62	1.00
	3/4	7.91	1.12
	4/5	8.64	1.20
	5/6	9.21	1.25
4(16APSK)	3/4	10.21	1.50
	4/5	11.03	1.60
	5/6	11.61	1.66
5(32APSK)	3/4	12.73	1.88
	4/5	13.64	2.00
	5/6	14.28	2.08

情况	CCM调制编码	时间利用率	CCM吞吐量/Mbps	VCM吞吐量/Mbps	吞吐量性能提升
情况1	QPSK 2/3	100%	26.7	50.8	90.2%
情况2	8PSK 5/6	74.8%	32.3	54.8	69.7%
情况3	32APSK 3/4	32.6%	20.3	52.9	160.6%

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Link adaptation algorithm based on LSTM network for LEO satellite

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