面向大尺度战场的信道仿真加速算法

刘畅; 李维实; 徐强; 时成哲; 邵士海

doi:10.11999/JEIT240655

面向大尺度战场的信道仿真加速算法

doi: 10.11999/JEIT240655

电子科技大学通信抗干扰全国重点实验室成都 611731

基金项目: 国家重点研发计划(2023YFF0717700)

详细信息

作者简介:
刘畅：男，博士生，研究方向为无线通信信号处理、无线信道建模等

李维实：男，博士生，研究方向为射频电路系统、无线信道建模等

徐强：男，副研究员，研究方向为无线通信信号处理、太赫兹通信技术等

时成哲：男，博士生，研究方向为通信抗干扰技术、相控阵天线技术等

邵士海：男，教授，博士生导师，研究方向为无线通信信号处理、抗干扰与安全通信等

通讯作者:
徐强　xuqiang06@uestc.edu.cn

中图分类号: TN92
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- 被引次数: 0
出版历程
- 收稿日期: 2024-07-26
- 修回日期: 2025-01-22
- 网络出版日期: 2025-01-25

Accelerated Channel Simulation Algorithm for Large-Scale Battlefield

National Key Laboratory of Wireless Communications, University of Electronic Science and Technology of China, Chengdu 611731, China

Funds: The National Key Research and Development Program of China (2023YFF0717700)

摘要

摘要: 大尺度战场环境中电磁频谱作战装备测试和演训需要依靠大规模数字化电磁环境进行仿真，然而大尺度电磁信道计算复杂度较高，难以提升计算速度。针对这一问题，该文提出一种迭代时域辐射度算法。该算法通过递推方法建模信道，利用空间相干性复用前一时刻的信道数据，并经过修正后用于当前时刻的信道计算。同时，采用面元信道搜索方法对面元中的信道进行低复杂度近似，有效降低了计算复杂度。仿真结果表明，与传统时域辐射度算法相比，所提算法在保证计算精度的基础上计算速度提升了1个数量级。与频域辐射度算法相比，所提算法的时延分辨率更高，更适用于大规模战场环境。
- 信道仿真 /
- 室外传播 /
- 辐射度算法
Abstract: Objective In large-scale battlefield environments, the testing and training of electromagnetic spectrum operation equipment rely on simulations within a vast digital electromagnetic environment. However, the computational complexity of large-scale electromagnetic channel simulations is high, hindering the improvement of computational speed. Traditional time-domain radiosity algorithms experience exponential growth in complexity with increasing reflection orders, while frequency-domain radiosity algorithms face limitations in time-delay resolution due to constraints in Fast Fourier Transform (FFT) points. This paper proposes an iterative time-domain radiosity algorithm that accelerates channel simulation, while maintaining high accuracy and time-delay resolution. Methods The proposed iterative time-domain radiosity algorithm uses a recursive modeling approach that reuses and corrects channel data from previous moments to reduce computational complexity. The algorithm begins by discretizing reflective surfaces in the environment into facets, which represent small, discrete elements capturing the reflective properties of the environment. The channel impulse response between the transmitter, facets, and receiver is modeled as a sum of direct and reflected components. The reflection process is described using shape factors that account for attenuation, delay, and visibility between facets, which are essential for accurately modeling interactions between the transmitter, facets, and receiver.To reduce computational complexity, the algorithm reuses channel data from the previous moment, leveraging small changes in the geographical location of the equipment between adjacent time steps. This reuse is possible due to the spatial coherence of the environment, ensuring that the previous channel data remains relevant with only minor adjustments. The channel data is then corrected by adjusting the delay and attenuation components based on changes in the direct shape factors between the transmitter and facets. This correction process ensures that the channel data remains accurate despite the reuse of prior calculations.The algorithm further employs a facet channel search method to approximate the channel by selecting the strongest reflection channels, thereby reducing the computational burden. This method involves identifying the top N strongest reflection channels within each facet, where N is determined by the desired balance between computational complexity and accuracy. By focusing on the strongest reflection channels, the algorithm significantly reduces the number of required calculations while maintaining high accuracy. The combination of data reuse, correction, and low-complexity approximation makes the proposed algorithm highly efficient for large-scale channel simulation. Results and Discussions Simulation results show that the proposed iterative time-domain radiosity algorithm improves computational speed by an order of magnitude, while maintaining accuracy, compared to the traditional time-domain radiosity algorithm (Fig. 10). This improvement is achieved by reusing and correcting channel data from previous moments, significantly reducing the number of recursive calculations required. The enhanced computational speed is particularly crucial in large-scale battlefield environments, where traditional algorithms struggle with high computational complexity.In comparison to the frequency-domain radiosity algorithm, the proposed algorithm provides higher time-delay resolution, making it better suited for large-scale battlefield environments (Fig. 8). The time-delay resolution of the frequency-domain radiosity algorithm is constrained by the number of FFT points, which must be set to a large value to achieve high resolution in large-scale environments. In contrast, the iterative time-domain radiosity algorithm maintains high time-delay resolution without the need for large FFT points, making it more efficient for large-scale simulations.The computational complexity of the iterative time-domain radiosity algorithm is significantly lower than that of both the traditional time-domain radiosity algorithm and the frequency-domain radiosity algorithm (Table 1). The traditional time-domain radiosity algorithm's complexity grows exponentially with the number of reflections, while the iterative algorithm reduces complexity by reusing and correcting previous calculations. The frequency-domain radiosity algorithm also faces high complexity due to the large number of FFT points required for high time-delay resolution. The proposed algorithm's ability to reduce computational complexity while maintaining accuracy makes it a highly effective solution for large-scale channel simulations.Furthermore, the iterative time-domain radiosity algorithm demonstrates high consistency with the traditional time-domain algorithm in terms of average delay and root mean square delay spread, with average deviations of 0.04% and 0.9%, respectively. This indicates that the proposed algorithm preserves high accuracy while significantly improving computational efficiency. Its ability to accurately model the channel's time-delay characteristics is critical for applications in large-scale battlefield environments, where precise channel simulation is essential for the effective testing and training of electromagnetic spectrum operation equipment. Conclusions The iterative time-domain radiosity algorithm proposed in this paper significantly enhances computational speed while maintaining accuracy and high time-delay resolution, addressing the computational challenges of channel simulation in large-scale battlefield environments. By reusing and correcting channel data from previous moments and employing a low-complexity approximation method, the algorithm reduces the computational burden without compromising accuracy. This makes it particularly well-suited for large-scale battlefield environments, where traditional algorithms struggle with high computational complexity and limited time-delay resolution. Future work could explore further optimizations and extend the algorithm to other electromagnetic environments, such as urban or indoor scenarios, where similar challenges in computational complexity and time-delay resolution may arise. Additionally, the algorithm could be adapted for real-time simulation systems, where rapid and accurate channel simulation is critical for decision-making and operational planning.
- Channel simulation /
- Outdoor propagation /
- Radiosity

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图 1 收发机与场景面元间的反射信道

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图 2 信道修正示意图

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图 3 修正迭代时域辐射度算法的计算偏差

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图 4 迭代时域辐射度算法系统框图

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图 5 战场仿真场景三角网络模型

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图 6 面元反射信道强度占比随着信道缓存长度的变化

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图 7 迭代辐射度算法面元中信道归一化衰减强度

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图 8 3种方法信道衰减和时延

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图 9 3种算法计算复杂度随面元数的变化

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图 10 3种算法计算时间随面元数的变化

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表 1 算法的计算复杂度

算法名称	计算复杂度
传统时域辐射度算法	$O({N^m})$
频域辐射度算法	$O({N^2} \cdot {P_{{\text{FFT}}}})$
迭代时域辐射度算法	$ O\left( {{N^2} \cdot L \cdot \left( {{{\log }_2}\left( {L - 1} \right){\text{ + 1}}} \right)} \right) $

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