Measurement and Modeling of Non-Stationary Channel in Tactical Mobile Ad Hoc Network
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摘要: 为研究城市战场环境下战术自组织网络（tactical mobile ad hoc network， TacMAN）通信节点间的信道特性，针对城区场景超短波频段展开信道测量与建模。测量结果表明，TacMAN通信场景下，多径信号存在明显的非平稳性和生灭现象。针对信道非平稳性，首先设计了一种多径识别与跟踪算法，有效地估计出多径信号的存在概率。其次，使用基于马尔科夫链的改进型抽头延迟线（tapped delay line， TDL）模型对实测数据的非平稳过程进行建模。最后基于赤池信息准则（akaike information criterion， AIC）给出了小尺度衰落的统计模型，分析表明双高斯混合分布（bimodal Gaussian mixture distribution， BGMD）和Ricean分布分别是描述模型首径与最强径幅度分布的最佳模型。论文建立的非平稳信道模型可以较好地描述TacMAN场景下信道特性，为提高相关军事通信场景下通信系统的性能提供参考。Abstract: This paper studies the channel characteristics between tactical mobile Ad Hoc network （TacMAN） communication nodes in urban battlefield environment. The channel measurements are performed at the very high frequency （VHF） band in urban areas. The measurement results show that multipath components exhibit obvious birth/death behaviors and non-stationary phenomenon in the region. Firstly， to represent the non-stationary， a multipath recognition and tracking algorithm is designed to estimate the existence probability of multipath components. Secondly， an improved tapped delay line （TDL） model based on Markov chain is used to model the non-stationary process of the measured data. Finally， the statistical model of small scale fading is chosen based on Akaike Information Criterion （AIC）. The bimodal Gaussian mixture distribution （BGMD） and Ricean distribution are found to be the optimal distribution for the first path and the strongest path of the channel model， respectively. The non-stationary channel model established in this paper can describe channel characteristics in TacMAN scenarios well， and provide reference for improving the performance of communication systems in related military communication scenarios.
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图 8 第1径幅度分布及 （a）Rayleigh、Ricean、Nakagami、Weibull分布拟合曲线；（b）BGMD拟合曲线
Figure 8. The amplitude distribution of the first channel tap and （a） Rayleigh， Ricean， Nakagami， Weibull fitting curves； （b） BGMD fitting curves
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图 9 第2径幅度分布及拟合曲线
Figure 9. The amplitude distribution of the second channel tap and the fitting curves
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图 11 实测数据与仿真数据的RMS-DS的CDF曲线比较
Figure 11. Comparison between the CDFs of the measured and the simulated RMS-DS
表 1 马尔可夫模型参数
Table 1 Markov model parameters
多径编号 S1 P00 P11 1 1 / 1 2 1 / 1 3 1 / 1 4 0.99 0 0.99 5 0.84 0.07 0.82 6 0.21 0.81 0.25 
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表 2 候选分布AIC权重
Table 2 AIC weights of the candidate distributions
多径编号 AIC权重 Rayleigh Ricean Nakagami Weibull BGMD 1 0 0 0 0.01 0.99 2 0.10 0.27 0.18 0.23 0.22 3 0.18 0.18 0.27 0.26 0.11 4 0.07 0.07 0.25 0.43 0.18 5 0.05 0.05 0.20 0.36 0.34 6 0.12 0.12 0.19 0.32 0.25
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表 3 测量城区MANET信道TDL模型
Table 3 The TDL model of measured urban MANET channel
多径编号 存在概率 S1 时延/ μs 功率/dB 最优拟合分布参数 1 1 0 -6.45 ω=0.65 μ=[0.02, 0.23] σ=[0.01, 0.12] 2 1 1.04 0 s=0.26, σ=0.14 3 1 1.72 -5.46 m=0.79, ω=0.03 4 0.99 2.79 -15.28 α=0.05, β=1.51 5 0.84 3.72 -22.49 α=0.02, β=1.46 6 0.21 4.73 -27.28 α=0.01, β=1.35
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