Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (10): 2994-3006.doi: 10.13229/j.cnki.jdxbgxb.20211319

Previous Articles     Next Articles

MIMO channel Characteristics analysis of AoA arbitrary distribution based on basic function fitting

Jie ZHOU(),Xue-ying Wang,Qian CHEN,Hong LUO,Lei XU   

  1. School of Electronic and Information，Nanjing University of Information Science and Technology，Nanjing 210044，China
  • Received:2021-12-01 Online:2023-10-01 Published:2023-12-13

RICH HTML

 0   

PDF (PC)

110

Abstract:

In view of the arbitrariness of the arrival signal， adopted the basis function sampling to establish the SFC model of MIMO system. Combined any spectral distribution of the approximate fitting algorithm was proposed， extended the calculation method of small angle spread to equivalently fitting arbitrary distribution. The measured data of urban propagation channel were verified by the piecewise exponential nonlinear fitting analysis. Therefore， the measured channel of MIMO under the approximate fitting solution was carried out， the impact of the sampling number and the weighting coefficient of the basis function on the accuracy was discussed. The research results show that the fitting method has a high degree of fit for the integration of the spectral distribution of the arbitrary arrival signal and the measured channel data. The proposed approximation algorithm can accurately fit the fading-related channel characteristics of the MIMO multi-antenna system， and can greatly reduce the complexity of calculation， improve the computational efficiency and save the simulation time. This method can effectively study the channel characteristics of MIMO systems in special scenarios， and can be extended to the analysis of Massive MIMO in multi-dimensional spatial domains.

Key words: communication technology, microcellular cities, small-angle extension, large-angle extension, spatial fading correlation, exponential nonlinear fitting

CLC Number: 

  • TN92

Fig.1

MIMO ULA linear array structure and scatterers"

Fig.2

Power spectrum basis function （Gaussian）sampling fitting diagram"

Fig.3

Dense urban channel measurement discrete data"

Fig.4

Exponential nonlinear regression fitting ofmeasured data"

Fig.5

Comparison of the theory and basis function sampling approximation algorithm under the area of ［0，180］"

Fig.6

Comparison of the theory and basis function sampling approximation algorithm under the area of ［185，225］"

Fig.7

Comparison of theory and basis function sampling approximation algorithm under the area of ［235，360］"

Table 1

Comparison of calculation efficiency of each model under the region ［0，180］"

简化模型耗时/s
N=72N=36N=24N=18
均匀0.001 2760.000 8650.000 9180.000 899
高斯0.253 3870.107 1190.093 7120.048 062
拉普拉斯0.000 9060.000 6040.000 7170.000 758
数值积分0.108 1160.117 7680.151 6110.104 656

Table 2

Comparison of calculation efficiency of each model under the region ［185，225］"

简化模型耗时/s
N=72N=36N=24N=18
均匀0.001 4120.000 8580.000 3270.000 248
高斯0.247 8550.114 9810.096 950.050 790
拉普拉斯0.000 8880.000 6120.000 2240.000 137
数值积分0.131 1530.122 2740.103 0290.120 447

Table 3

Comparison of calculation efficiency of each model under the region ［235，360］"

简化模型耗时/s
N=72N=36N=24N=18
均匀0.001 2560.000 8920.000 3190.000 170
高斯0.232 8370.104 6280.085 2030.048 521
拉普拉斯0.000 7400.000 7380.000 2060.000 204
数值积分0.449 1520.129 6270.152 4460.113 273

Fig.8

Simplified model error performance comparison under region ［0，180］"

Fig.9

Simplified model error performance comparison under region ［185，225］"

Fig.10

Simplified model error performance comparison under region ［235，360］"

Table 4

Comparison of the mean absolute error of each model under the region ［0，180］"

简化模型绝对误差均值
Δs=5°Δs=10°Δs=15°Δs=20°
均匀0.064 440.089 810.106 200.108 80
高斯0.007 020.023 530.044 170.064 86
拉普拉斯0.045 840.080 340.108 760.110 71

Table 5

Comparison of the mean absolute error of each model under the region ［185，225］"

简化模型绝对误差均值
Δs=5°Δs=10°Δs=15°Δs=20°
均匀0.042 870.060 510.070 760.073 52
高斯0.038 520.039 320.039 680.040 06
拉普拉斯0.013 470.043 050.071 830.071 88

Table 6

Comparison of the mean absolute error of each model under the region ［235，360］"

简化模型绝对误差均值
Δs=5°Δs=10°Δs=15°Δs=20°
均匀0.064 490.089 850.106 250.108 85
高斯0.007 030.023 550.044 210.064 90
拉普拉斯0.045 890.080 390.108 810.110 77
1 Elayoubi S E, Fallgren M, Spapis P, et al. 5G Service requirements and operational use cases: analysis and METIS II vision[C]∥European Conference on Networks & Communications, Athens, Greek, 2016: 158-162.
2 郭文祥, 余志勇, 逄晨,等. 认知无线电频谱感知技术综述[J]. 通信技术, 2018, 51(2): 261-265.
Guo Wen-xiang, Yu Zhi-yong, Pang Chen, et al.Overview of cognitive radio spectrum sensing technology[J]. Communication Technology,2018, 51(2):261-265.
3 赵水静. 大规模MIMO-OFDM系统的双稀疏信道建模与估计方法研究[D].南京: 南京邮电大学通信与信息工程学院, 2020.
Zhao Shui-jing. Research on double sparse channel modeling and estimation method for massive MIMO-OFDM system[D]. Nanjing: School of Communication and Information, Nanjing University of Posts and Telecommunications, 2020.
4 肖大家. 5G密集网络中的干扰协调技术研究[D]. 南京: 东南大学通信工程学院, 2018.
Xiao Da-jia. Research on interference coordination technology in 5G dense network[D]. Nanjing: College Communication Engineering,Southeast University, 2018.
5 林鑫. 第5代移动通信网络的新业务及其关键技术分析[J]. 信息通信, 2018(11): 259-261.
Lin Xin. New business and key technology analysis of the 5th generation mobile communication network[J]. Information and Communication, 2018(11): 259-261.
6 黄俊然. 无线信道测量与建模中实测数据拟合的研究[D]. 天津: 天津大学天津大学电器自动化与信息工程学院, 2010: 7-8.
Huang Jun-ran. Fitting of measured data in wireless channel measurement and modeling[D]. Tianjin: School of Electrical Automation and Information Engineering,Tianjin University, 2010: 7-8.
7 闭宇铭. 非平稳无线信道建模及其仿真技术研究[D]. 北京: 北京邮电大学信息与通信工程学院,2017: 16-20.
Bi Yu-ming. Research on modeling of non-stationary wireless channel and its simulation technique[D]. Beijing: School of Information and Communication Engineering,Beijing University of Postsand Telecommunications, 2017: 16-20.
8 Fleury B H. First and second-order character- ization of direction dispersion and space selectivity in the radio channel[J]. IEEE Transactions on Information Theory, 2000, 46(6): 2027-2044.
9 Ziółkowski Cezary, Kelner J M. Empirical models of the azimuthal reception angle—Part I: comparative analysis of empirical models for different propagation environments[J]. Wireless Personal Communi, 2016, 91(2): 771-791.
10 Xiao H, Nie Z. Low spatial correlation at base station uniform linear antennas[C]∥International Conference on Communications, Circuits and Systems, Guilin, China,2006: 785-787.
11 李忻, 聂在平. MIMO信道中衰落信号的空域相关性评估[J]. 电子学报, 2004(12): 1949-1953.
Li Xin, Nie Zai-ping. Spatial correlation evaluation of fading signals in MIMO channels[J]. Chinese Journal of Electronics, 2004(12): 1949-1953.
12 Abdul Waheed Umrani. Performance analyses of spatial diversity and beam-forming for wireless communication systems[D].Pakistan:United Arab Emirates 2008:39-65.
13 Yong S K, Honpson J S. Three dimensional spatial fading correlation model for compact MIMO receivers[J]. IEEE Trans. Wireless Communications, 2005, 4(6): 2856-2869.
14 Gutierrez C A, Patzold M. Sum of sinusoids based simulation of flat fading wireless propagation channels under non-isotropic scattering conditions[C]∥Global Telecom Conference,Washington D.C., USA, 2007: 3842-3846.
15 Forenza A, Love D J, Heath R W. Simplified spatial correlation models for clustered MIMO channels with different array configurations[J]. IEEE Transactions on Vehicular Technology, 2007, 56(4): 1924-1934.
16 周杰, 邹士娇, 陈珍. 衰落相关信道近似算法及其Massive MIMO系统分析[J]. 新疆大学学报: 自然科学版, 2018, 35(3): 100-111.
Zhou Jie, Zou Shi-jiao, Chen Zhen. Decay correlation channel approximation algorithm and its Massive MIMO system analysis[J]. Journal of Xinjiang University (Natural Science Edition), 2018, 35(3): 100-111.
17 周杰, 王亚林, 菊池久和. 多天线信道空间衰落相关性近似算法及其复杂性研究[J]. 物理学报, 2014, 63(23): 1-12.
Zhou Jie, Wang Ya-lin, Hisakazu Kikuchi. Spatial decay correlation approximation algorithm for multi-antenna channel and its complexity study[J]. Phys J, 2014, 63(23): 1-12.
18 Goldsmith A, Jafar S A, Jindal N, et al. Capacity limits of MIMO channels[J]. IEEE JSAC, 2003, 21(5): 684-702.
19 朱秋明, 徐大专, 罗艳强, 等. 多输入多输出信道空域相关性评估简化模型[J]. 电波科学学报, 2011, 26(2): 203-208.
Zhu Qiu-ming, Xu Da-zhuan, Luo Yan-qiang, et al. A simplified model for the evaluation of multi-input-multi-output channel airspace correlation[J]. Journal of Radio Frequency Science, 2011, 26(2): 203-208.
20 高凯,张尔扬. MIMO信道的空间相关特性及信道容量分析[J]. 电子与信息学报,2007, 29(7): 1542-1545.
Gao Kai, Zhang Er-yang. Spatial correlation characteristics and channel capacity analysis of MIMO channel[J]. Journal of Electronics and Information Science, 2007, 29(7): 1542-1545.
21 Sieskul B T, Kupferschmidt C, Kaiser T. Spatial fading correlation for semicircular scattering: angular spread and spatial frequency approximations[C]∥International Conference on Communications & Electronics, Nha Trang, Vietnam, 2010: 216-221.
22 Lee W C Y. Effects on correlation between two mobile radio base-station antennas[J]. IEEE Trans Commun, 1973, 21(11): 1214-1224.
23 Eyeynis Gradsht, Kim Ryzh. Table of integrals, series and products[M]. New York: Academic Press, 1980.
24 Jie Z, Zhigang C, Kikuchi H. Analysis of MIMO antenna array based on 3D Von Mises Fisher distribution[J]. Journal of China Universities of Posts and Telecommunications, 2015, 22(2): 1-12.
25 Jie Z, Kenta I, Shigenobu S. Generalized spatial correlation equations for antenna arrays in wireless diversity reception: exact and approximate analyses[J]. IEICE Trans Communications, 2004, 84(5): 1-5.
26 周杰, 曹志钢, 菊池久和. 非对称空间统计信道模型及其MIMO多天线系统[J]. 东南大学学报: 自然科学版, 2014, 44(2): 232-238.
Zhou Jie, Cao Zhi-gang, Kikuchi Hisakazu. Asymmetric spatial statistical channel model and its MIMO multi-antenna system[J]. Journal of Southeast University(Natural Science Edition),2014, 44(2): 232-238.
27 陈钱, 周杰, 邵根富. 角度域任意功率谱MIMO信道特征计算[J]. 计算机科学, 2020, 47(6): 271-275.
Chen Qian, Zhou Jie, Shao Gen-fu. MIMO channels with arbitrary AoA power spectrum for various wireless environments[J]. Computer Science, 2020, 47(6): 271-275.
28 Ghoraishi M, Takada J I, Imai T. A pseudo-geometrical channel model for dense urban line-of-sight street microcell[C]∥The 17th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), Helsinki, Finland, 2006:1-5.
29 Ghoraishi H, Jun-ichi J, Takada J, et al. A single bounce channel model for dense urban street microcell[C]∥URSI-Japan Radio Science Meeting, Tokyo,Japan, 2006:1-5.
[1] Qiang GUO,Yu-qiang CUI,Yong WANG. Nodes scheduling algorithm based on dynamic cluster in wireless sensor network [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(6): 1466-1476.
[2] Jian-po LI,Mei-lin LI,Tao YANG,Peng XUE. Low-complexity Wiener filter channel estimation algorithm in massive MIMO-OFDM system [J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(1): 211-218.
[3] Da-yang SUN,Xue-ying WANG,Shuang-xue HAN,Hui ZHONG,Jiang-nan DAI. Non⁃line⁃of⁃sight identification and optimization based on virtual coordinates of anchors [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(6): 2207-2215.
[4] Jian-po LI,Peng XUE,Tao YANG,Mei-lin LI. Pilot contamination suppression method for massive MIMO system based on divided pilot reuse [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(6): 2225-2236.
[5] Yi LIU,Ling-ling XIAO,Gai-jing WANG,Wu-jun ZHANG. Resource allocation algorithm based joint optimization for D2D communications in cellular networks [J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(1): 306-314.
[6] Cui-ran LI,Yong-sheng YU,Jian-li XIE. Dynamic game algorithm for spectrum sharing based on priority of secondary users [J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(1): 315-323.
[7] Jin-peng WANG,Zheng-peng YE,Fan CAO,Nian-yu ZOU. Cooperative distributed antenna transmission method based on co-channel interference in 5G mobile communication system [J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(1): 333-341.
[8] Wen-jun LI,Qiang HUA,Li-dong TAN,Yue SUN. An improved algorithm for combination of DV-HOP and RSSI [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(5): 1689-1695.
[9] Hong-yan WANG,Yun-fei FANG,Sheng-qi ZHU,Bing-nan PEI. DOA estimation method considering mutual coupling effect in presence of non⁃uniform noise [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(5): 1706-1714.
[10] Yong LIU,Fang⁃shun DENG,Xiao⁃lin LIU,Si⁃jie MIN,Peng WANG. Frequency estimation of minimum shift keying signal based on dual chaotic oscillator [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(4): 1357-1362.
[11] Hong⁃zhi WANG,Fang⁃da JIANG,Ming⁃yue ZHOU. Power allocation of cognitive radio system based on genetic particle swarm optimization [J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(4): 1363-1368.
[12] ZHOU Yan-guo,ZHANG Hai-lin,CHEN Rui-rui,ZHOU Tao. Two-level game approach based resource allocation scheme in cooperative networks [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1879-1886.
[13] SUN Xiao-ying, HU Ze-zheng, YANG Jin-peng. Assessment method of electromagnetic pulse sensitivity of vehicle engine system based on hierarchical Bayesian networks [J]. 吉林大学学报(工学版), 2018, 48(4): 1254-1264.
[14] DONG Ying, CUI Meng-yao, WU Hao, WANG Yu-hou. Clustering wireless rechargeable sensor networks charging schedule based on energy prediction [J]. 吉林大学学报(工学版), 2018, 48(4): 1265-1273.
[15] DING Ning, CHANG Yu-chun, ZHAO Jian-bo, WANG Chao, YANG Xiao-tian. High-speed CMOS image sensor data acquisition system based on USB 3.0 [J]. 吉林大学学报(工学版), 2018, 48(4): 1298-1304.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright © Journal of Jilin University(Engineering and Technology Edition), All Rights Reserved.
Tel: +86-431-85095297　Fax: +86-431-85094074　E-mail: xbgxb@jlu.edu.cn
Powered by Beijing Magtech Co. Ltd