吉林大学学报(工学版) ›› 2023, Vol. 53 ›› Issue (10): 2994-3006.doi: 10.13229/j.cnki.jdxbgxb.20211319

• 通信与控制工程 • 上一篇    下一篇

基于函数拟合AoA任意分布的MIMO信道特征分析

周杰(),王学英,陈钱,罗宏,徐蕾   

  1. 南京信息工程大学 电子与信息工程学院,南京 210044
  • 收稿日期:2021-12-01 出版日期:2023-10-01 发布日期:2023-12-13
  • 作者简介:周杰(1964-),男,教授,博士.研究方向:无线通信,Massive MIMO. E-mail:zhoujienuist@139.com
  • 基金资助:
    国家自然科学基金项目(61971167)

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

摘要:

针对波达信号谱的任意性,采用角度域基函数采样法,建立多入多出(MIMO)天线衰落空间相关性(SFC)计算模型。提出了以基函数展开法,加权叠加形成符合多样无线场景波达信号近似信号到达角(AoA)复杂分布函数,导出了完整理论计算式。并对微蜂窝城市传播信道的实测数据进行了验证和分段指数非线性拟合分析,导出了实测信道下的MIMO SFC的近似拟合解,探讨了基函数采样数和加权系数的选取对计算精度的影响。研究结果表明此拟合法对任意波达信号谱分布和信道实测数据的整合有较高的拟合度,提出的近似算法可准确地拟合MIMO多天线系统的衰落相关信道特征,且能大幅度降低其计算的复杂度,提高运算效率,节省了仿真时间。此法能有效研究MIMO系统在特殊场景中的信道特征,并可推广到在多维空间域中对Massive MIMO的分析。

关键词: 通信技术, 蜂窝城市, 小角度扩展, 大角度扩展, 衰落空间相关性, 指数非线性拟合

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

中图分类号: 

  • TN92

图1

MIMO ULA线阵结构与散射体"

图2

功率谱基函数(高斯)采样拟合图"

图3

密集市区信道测量离散数据"

图4

实测数据的指数非线性回归拟合"

图5

[0,180]区域下理论与基函数采样近似算法比较"

图6

[185,225]区域下理论与基函数采样近似算法比较"

图7

[235,360]区域下理论与基函数采样近似算法比较"

表1

[0,180]区域下各模型运算效率比较"

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

表2

[185,225]区域下各模型运算效率比较"

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

表3

[235,360]区域下各模型运算效率比较"

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

图8

[0,180]区域下简化模型误差性能比较"

图9

[185,225]区域下简化模型误差性能比较"

图10

[235,360]区域下简化模型误差性能比较"

表4

[0,180]区域下各模型绝对误差均值的比较"

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

表5

[185,225]区域下各模型绝对误差均值的比较"

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

表6

[235,360]区域下各模型绝对误差均值的比较"

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
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