Accelerated Channel Simulation Algorithm for Large-Scale Battlefield

LIU Chang; LI Weishi; XU Qiang; SHI Chengzhe; SHAO Shihai

doi:10.11999/JEIT240655

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LIU Chang, LI Weishi, XU Qiang, SHI Chengzhe, SHAO Shihai. Accelerated Channel Simulation Algorithm for Large-Scale Battlefield[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240655

Citation:

LIU Chang, LI Weishi, XU Qiang, SHI Chengzhe, SHAO Shihai. Accelerated Channel Simulation Algorithm for Large-Scale Battlefield[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT240655

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Accelerated Channel Simulation Algorithm for Large-Scale Battlefield

doi: 10.11999/JEIT240655

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)

Received Date: 2024-07-26
Rev Recd Date: 2025-01-13

Available Online: 2025-01-25

Abstract

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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References(16)

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