复杂气象环境海上无线通信信道衰落估计模型

doi:10.11959/j.issn.1000-0801.2022061

摘要/Abstract

摘要：

海洋表面受电磁波稀疏散射、海浪阴影衰落、舰船摆动、晴雾雨雪等复杂环境因素影响，海上无线通信信道衰落建模极其复杂。通过分析直射、镜面反射、漫反射和复杂气象环境对信道衰落的影响，提出了复杂气象环境海上无线通信信道衰落估计模型。设计改进蜂群算法对该信道衰落估计模型求解，构建以sin(·)函数为算子引导跟随蜂进行搜索，降低搜索空间，提高求解效率和精度。引领蜂以概率P接收侦查蜂解进行更新，抑制求解算法陷入局部最优的问题。实验结果表明，提出的模型与波动双射线模型和地球曲率模型相比，精度平均提升13.58%和11.43%；与布谷鸟搜索、遗传算法、模拟退火算法等对比分析，搜索时间分别减少60.48%、45.18%、43.23%。

关键词: 海洋环境, 无线通信, 蜂群算法, 信道衰落

Abstract:

Due to the influence of complex marine environmental factors such as sparse scattering of electromagnetic wave on the ocean surface, wave shadow fading, complex meteorological environment.It is extremely hard to model the fading of wireless channel in maritime communication environment.After analyzing the influence of meteorological environment and multipath channel fading via direct-path, specular-path and diffuse-path on marine wireless communication channel fading, the estimation of wireless channel fading model in maritime communication for complex meteorological environment (EWCFM-CME) was proposed.An improved bee colony algorithm was designed to estimation the parameter of EWCFM-CME.In order to improve the efficiency and accuracy of search algorithm, an iterative function with sin(·) function as operator was designed in search scheme of bee colonies.The leading bee received the detection bee solution to update itself with probability P.It prevents the solution algorithm from falling into local optimization.The simulation results show that, compared with fluctuate two-ray (FTR) fading model and round earth loss (REL) model, the mean absolute error (MAE) of EWCFM-CME increased by 13.58% and 11.43% By comparing EWCFM-CME with cuckoo search, genetic algorithm and simulated annealing algorithm, the search time is reduced by 60.48%, 45.18% and 43.23%, respectively.

Key words: marine environment, wireless communication, bee colony algorithm, channel fading

中图分类号:

TP393

戴亚盛, 马柏林, 乐光学. 复杂气象环境海上无线通信信道衰落估计模型[J]. 电信科学, 2022, 38(3): 158-171.

Yasheng DAI, Bolin MA, Guangxue YUE. Estimation of wireless channel fading model in maritime communication for complex meteorological environment[J]. Telecommunications Science, 2022, 38(3): 158-171.

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类别	参数	值（单位）
发射机、接收机仿真参数	载波频率	2.2～2.4 GHz、5.1～5.3 GHz
	频带宽度	200 MHz
	发射功率	30～35 dBm
	接收功率	-75～10 dBm
	多普勒频偏分辨率	7 Hz
	舰船移动最大多普勒频偏	300 Hz
	直射与镜面反射平均到达延迟差	12.96 μs
	发射天线个数	8个
	发射天线高度	24.8 m
	发射天线增益	18 dBi
	接收天线个数	3个
	接收天线高度	12.5 m
	接收天线增益	11 dBi
	调制方式	N-QAM，N∈{2,4,8,16}
	光速	299 792 458 m/s
海上通信环境仿真参数	温度	10～30℃
	风速	3～6 m/s
	海浪最大高度	1.1 m
	海浪平均高度	0.6 m
	海浪高度均方差	0.1 m
	海水相对介电常数	81
相对移动数据集参数	采样间隔	4～5 s
	采样时间	5h
	采样次数	4 000
	实际样本容量	3 572
	气温	11～26 ℃
	通信距离	200 m～29 km
固定位置数据集参数	采样间隔	1s
	采样时间	1h
	采样次数	3 600次
	采样开始时刻	数据集2a清晨6点、数据集2b下午1点
	实际样本容量	3 123（数据集2a）/3 206（数据集2b）
	气温	11℃（数据集2a）/26℃（数据集2b）
	通信距离	29 km

模型	向基站方向航行					离开基站方向航行
模型	MAE	RMSE	迭代次数/次	算法运行时间/ms	MAE>0.9距离/km	MAE	RMSE	迭代次数/次	算法运行时间/ms	MAE>0.9距离/km
FTR	0.818	0.827	211	167	4	0.797	0.806	406	331	3
EWCFM-CME	0.903	0.912	52	66	15	0.891	0.901	85	101	19
REL	0.783	0.792	86	317	4	0.777	0.786	107	446	3

模型	固定位置测试1					固定位置测试2
模型	MAE	RMSE	迭代次数/次	运行时间/ms	稳定时间/ms	MAE	RMSE	迭代次数/次	运行时间/ms	稳定时间/ms
FTR	0.576	0.585	525	615	585	0.492	0.501	538	661	593
EWCFM-CME	0.851	0.860	96	114	93	0.832	0.841	101	124	96
REL	0.582	0.591	221	354	330	0.513	0.522	237	361	341

模型	测试次数	MAE	RMSE
Rayleigh	1 000	0.351	0.432
Rician	1 000	0.645	0.735
Nakagami	1 000	0.585	0.675
Weibull	1 000	0.535	0.645
TWDP	1 000	0.564	0.653
Gaussian	1 000	0.323	0.433

模型	测试次数	最优解平均MAE	MAE达到0.9概率	收敛时间/ms	解空间搜索覆盖率	线程数	测试运行时间/s
EWCFM-CME	1 000	0.914	95.6%	32	52%	4	135
布谷鸟搜索	1 000	0.911	96.3%	455	96%	4	215
遗传搜索算法	1 000	0.904	97.8%	335	97%	1	1 535
贝叶斯概率估计	1 000	0.731	0	1	/	1	10
梯度下降	1 000	0.766	1.3%	3	<0.1%	1	11
模拟退火	1 000	0.90	50.7%	267	51%	1	1 663