基于经验知识遗传算法优化的神经网络模型实现时间反演信道预测

院琳; 杨雪松; 王秉中

doi:10.7498/aps.68.20190327

摘要

人工神经网络由于具有较强的非线性拟合能力, 可用来建立终端位置与接收信号之间的映射关系, 从而获得不同位置的信道特性. 神经网络建模的精度一般由所使用的训练样本数量决定, 训练样本数目越多, 模型往往越精确. 但大量的训练数据的获取, 耗时较多. 本文将经验知识融入遗传算法, 对人工神经网络模型进行优化, 实现了时间反演电磁信道的快速建模. 通过提取时间反演信号的传播参数, 并将其作为经验知识用于遗传算法的适应度函数, 来优化神经网络模型的权值和阈值. 在保证训练样本数量不变的情况下, 相比直接利用神经网络建模, 提高了建模的精度. 以一种简单的室内时间反演场景为例, 验证了方法的有效性.

关键词:

Abstract

Because of the strong non-linear fitting capability, the artificial neural network (ANN) can be used to establish the mapping relationship between the terminal position and the received signal for obtaining the channel characteristics at different locations. The accuracy of an ANN model is, in general, determined by the number of the training sets used in constructing the model. The more the training sets, the better the accuracy is. However, getting a large number of training sets by deterministic model is expensive. Therefore, under the same number of training sets, improving the accuracy of the model is crucial to develop an effective time reversal (TR) modeling method based on ANN. In this paper, a new TR channel modeling method based on the back propagation neural network is proposed. Genetic algorithm (GA) with excellent global search capability is used to optimize the weight and threshold of the ANN to avoid the possibility of the ANN falling into local minimum. According to the basic principle of time reversal, the peak characteristics are obtained by the fitting method. In order to improve the accuracy of the model, the peak value characteristics are integrated into the GA as empirical knowledge to change the fitness function. Meanwhile, the principal component analysis technology is utilized to process data, which reduces the data dimension and the training time of ANN while data characteristics are ensured. Once the terminal antenna positions are input to the proposed model, the accurate TR received signals can be quickly obtained. Finally, the deconvolution operation of the received signal is performed by the clean algorithm to obtain the channel characteristics. A simple indoor TR scenario is used as an example to demonstrate the effectiveness of the proposed method. The results show that the three channel characteristics obtained by the model, i.e., channel impulse response peak value, 15 dB multipath number, and average delay, have high accuracy. Furthermore, the proposed model has more excellent performance than the other two ANN models under the condition of the same number of training samples. Based on the basic principle of TR technology, the electromagnetic waves have better focusing effect in more complex environments. Therefore, the proposed method is also applicable to more complicated environments than the simple indoor scenario.

Keywords:

作者及机构信息

电子科技大学物理学院, 成都　610054

通信作者: 杨雪松, xsyang@uestc.edu.cn

基金项目: 国家自然科学基金(批准号: 61331007)资助的课题

Authors and contacts

School of Physics,University of Electronic Science and Technology of China, Chengdu 610054, China

Corresponding author: Yang Xue-Song, xsyang@uestc.edu.cn

Funds: Project supported by the National Natural Science Foundation of China (Grant No. 61331007)

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参考文献

[1]	Hoefer W J R 2015 IEEE Trans. Microw. Theory 63 3 Google Scholar
[2]	Rosny J, Lerosey G, Fink M 2010 IEEE Trans. Antennas Propag. 58 3139 Google Scholar
[3]	Lerosey G, Rosny J, Tourin A, Derode A, Fink M 2006 Appl. Phys. Lett. 88 154101 Google Scholar
[4]	Bellizzi G G, Bevacqua M T, Crocco L, Isernia T 2018 IEEE Trans. Antennas Propag. 66 4380 Google Scholar
[5]	Carminati R, Pierrat R, Rosny J, Fink M 2007 Opt. Lett. 32 3107 Google Scholar
[6]	Malyuskin O, Fusco V 2010 IET Microw. Antenna. P. 4 1140 Google Scholar
[7]	臧锐, 王秉中, 丁帅, 龚志双 2016 物理学报 65 204102 Google Scholar Zang R, Wang B Z, Ding S, Gong Z S 2016 Acta Phys. Sin. 65 204102 Google Scholar
[8]	龚志双, 王秉中, 王任, 臧锐, 王晓华 2017 物理学报 66 044101 Google Scholar Gong Z S, Wang B Z, Wang R, Zang R, Wang X H 2017 Acta Phys. Sin. 66 044101 Google Scholar
[9]	Ge G D, Wang B Z, Wang D, Zhao D S, Ding S 2011 IEEE Trans. Antennas Propag. 59 4345 Google Scholar
[10]	Abduljabbar A M, Yavuz M E, Costen F, Himeno R, Yokota H 2016 IEEE Trans. Antennas Propag. 64 3636 Google Scholar
[11]	Naqvi I H, Zein G, Lerosey G, Rosny J, Besnier P, Tourin A, Fink M 2010 IET Microw. Antenna. P. 4 643 Google Scholar
[12]	Yang Y, Wang B Z, Ding S 2016 Chin. Phys. B 25 050101 Google Scholar
[13]	Ding S, Fang Y, Zhu J F, Yang Y, Wang B Z 2019 IEEE Trans. Antennas Propag. 67 1386 Google Scholar
[14]	朱江, 王雁, 杨甜 2018 物理学报 67 050201 Google Scholar Zhu J, Wang Y, Yang T 2018 Acta Phys. Sin. 67 050201 Google Scholar
[15]	Talebi F, Pratt T 2016 IEEE Trans. Veh. Technol. 65 499 Google Scholar
[16]	Sarestoniemi M, Hamalainen M, Iinatti J 2017 IEEE Access 5 10622 Google Scholar
[17]	Naqvi I H, Besnier P, Zein G 2011 IET Microw. Antenna P. 5 468 Google Scholar
[18]	Naqvi I H, Aleem S A, Usman O, Ali S B, Besnier P, Zein G 2012 IEEE Wireless Communications and Networking Conference Shanghai, China, April 1−4, 2012 p37
[19]	Kim H, Sui C, Cai K, Sen B, Fan J 2018 IEEE Trans. Electromagn. Compat. 60 1648 Google Scholar
[20]	Popoola S I, Misra S, Atayero A A 2018 Wireless Pers. Commun. 99 441 Google Scholar
[21]	Qiu R C, Zhou C M, Guo N, Zhang J Q 2006 IEEE Antenn. Wirel. Pr. 5 269 Google Scholar
[22]	王秉中, 臧锐, 周洪澄 2013 微波学报 29 22 Google Scholar Wang B Z, Zang R, Zhou H C 2013 J. Microw. 29 22 Google Scholar
[23]	Tse D, Viswanath P 2005 Fundamentals of Wireless Communication (Cambridge: Cambridge University Press) p12
[24]	Cramer J M, Scholtz R A, Win M Z 2002 IEEE Trans. Antennas Propag. 50 561 Google Scholar
[25]	Chen J F, Li Y H, Wang J Q, Li Y J, Zhang Y L 2015 IET Image Process. 9 218 Google Scholar
[26]	Kaina N, Dupre M, Lerosey G, Fink M 2014 Sci. Rep. 4 6693

施引文献

图 1 结合PCA技术的神经网络模型结构

Fig. 1. Structure of the neural network model combined with PCA.

下载: 全尺寸图片幻灯片

图 2 建立GA-BP模型的流程图

Fig. 2. Flowchart of the GA-BP model development process.

下载: 全尺寸图片幻灯片

图 3 信道特性获取流程图

Fig. 3. Flowchart of the proposed model to obtain channel characteristic.

下载: 全尺寸图片幻灯片

图 4 仿真场景俯视图

Fig. 4. Top view of the simulation scene.

下载: 全尺寸图片幻灯片

图 5 11阶多项式拟合结果与仿真数据的对比

Fig. 5. Comparison of the results of 11th order polynomial fitting and simulation data.

下载: 全尺寸图片幻灯片

图 6 利用本模型获得接收信号与仿真获得信号的对比　(a)测试样本1; (b)测试样本2

Fig. 6. Comparison of the signals of the proposed model and simulation:　(a) Test sample #1; (b) test sample #2

下载: 全尺寸图片幻灯片

图 7 采用不同模型得到的信道冲激响应峰值对比

Fig. 7. Comparison of different modeling methods for the channel impulse response peaks.

下载: 全尺寸图片幻灯片

表 1 TRM天线位置

Table 1. Location of the TRM antennas.

	X/cm	Y/cm	Z/cm
TRM1	2.5	0	0
TRM2	–2.5	0	0
TRM3	7.5	0	0
TRM4	–7.5	0	0

下载: 导出CSV

表 2 终端天线的位置

Table 2. Location of the terminal antenna.

	坐标最小值/cm	坐标最大值/cm
X	10	30
Y	10	30
Z	0	30

下载: 导出CSV

表 3 CPU时间及计算机性能

Table 3. CPU time and computer performance.

	本模型	FDTD软件仿真	CST仿真软件
CPU时间	约2 min 11 s	约23 h	约25 h
计算平台	Intel i5-4430 3.00 GHz 16 GB(台式机)	E5-2690v3 2.60 GHz 128 GB(服务器)	E5-2690v3 2.60 GHz 128GB(服务器)

下载: 导出CSV

[1]	Hoefer W J R 2015 IEEE Trans. Microw. Theory 63 3 Google Scholar
[2]	Rosny J, Lerosey G, Fink M 2010 IEEE Trans. Antennas Propag. 58 3139 Google Scholar
[3]	Lerosey G, Rosny J, Tourin A, Derode A, Fink M 2006 Appl. Phys. Lett. 88 154101 Google Scholar
[4]	Bellizzi G G, Bevacqua M T, Crocco L, Isernia T 2018 IEEE Trans. Antennas Propag. 66 4380 Google Scholar
[5]	Carminati R, Pierrat R, Rosny J, Fink M 2007 Opt. Lett. 32 3107 Google Scholar
[6]	Malyuskin O, Fusco V 2010 IET Microw. Antenna. P. 4 1140 Google Scholar
[7]	臧锐, 王秉中, 丁帅, 龚志双 2016 物理学报 65 204102 Google Scholar Zang R, Wang B Z, Ding S, Gong Z S 2016 Acta Phys. Sin. 65 204102 Google Scholar
[8]	龚志双, 王秉中, 王任, 臧锐, 王晓华 2017 物理学报 66 044101 Google Scholar Gong Z S, Wang B Z, Wang R, Zang R, Wang X H 2017 Acta Phys. Sin. 66 044101 Google Scholar
[9]	Ge G D, Wang B Z, Wang D, Zhao D S, Ding S 2011 IEEE Trans. Antennas Propag. 59 4345 Google Scholar
[10]	Abduljabbar A M, Yavuz M E, Costen F, Himeno R, Yokota H 2016 IEEE Trans. Antennas Propag. 64 3636 Google Scholar
[11]	Naqvi I H, Zein G, Lerosey G, Rosny J, Besnier P, Tourin A, Fink M 2010 IET Microw. Antenna. P. 4 643 Google Scholar
[12]	Yang Y, Wang B Z, Ding S 2016 Chin. Phys. B 25 050101 Google Scholar
[13]	Ding S, Fang Y, Zhu J F, Yang Y, Wang B Z 2019 IEEE Trans. Antennas Propag. 67 1386 Google Scholar
[14]	朱江, 王雁, 杨甜 2018 物理学报 67 050201 Google Scholar Zhu J, Wang Y, Yang T 2018 Acta Phys. Sin. 67 050201 Google Scholar
[15]	Talebi F, Pratt T 2016 IEEE Trans. Veh. Technol. 65 499 Google Scholar
[16]	Sarestoniemi M, Hamalainen M, Iinatti J 2017 IEEE Access 5 10622 Google Scholar
[17]	Naqvi I H, Besnier P, Zein G 2011 IET Microw. Antenna P. 5 468 Google Scholar
[18]	Naqvi I H, Aleem S A, Usman O, Ali S B, Besnier P, Zein G 2012 IEEE Wireless Communications and Networking Conference Shanghai, China, April 1−4, 2012 p37
[19]	Kim H, Sui C, Cai K, Sen B, Fan J 2018 IEEE Trans. Electromagn. Compat. 60 1648 Google Scholar
[20]	Popoola S I, Misra S, Atayero A A 2018 Wireless Pers. Commun. 99 441 Google Scholar
[21]	Qiu R C, Zhou C M, Guo N, Zhang J Q 2006 IEEE Antenn. Wirel. Pr. 5 269 Google Scholar
[22]	王秉中, 臧锐, 周洪澄 2013 微波学报 29 22 Google Scholar Wang B Z, Zang R, Zhou H C 2013 J. Microw. 29 22 Google Scholar
[23]	Tse D, Viswanath P 2005 Fundamentals of Wireless Communication (Cambridge: Cambridge University Press) p12
[24]	Cramer J M, Scholtz R A, Win M Z 2002 IEEE Trans. Antennas Propag. 50 561 Google Scholar
[25]	Chen J F, Li Y H, Wang J Q, Li Y J, Zhang Y L 2015 IET Image Process. 9 218 Google Scholar
[26]	Kaina N, Dupre M, Lerosey G, Fink M 2014 Sci. Rep. 4 6693

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