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<p>With the growth of the demand for wireless communication systems, when the existing algebraic model processes real-time data, the frequency adjustment is inflexible, it is difficult to quickly optimize the transmission parameters to cope with network load changes, and the long-term operation fails to effectively control the CPU frequency, resulting in increased energy consumption. To better promote the development of wireless communication systems, this article aimed to use aerial computing architecture to optimize the linear algebraic model of communication transmission frequency, to better meet the needs of today's wireless communication systems. The article first designed a communication frequency stabilization device structure to ensure high stability of the output spectrum. Then it introduced a dynamic frequency adjustment module to achieve real-time adjustment of communication transmission frequency. It then improved the data transmission rate through the design of the aerial computing perception module. This article used a frequency optimization algorithm based on linear algebra to adjust its linear relationship, optimize transmission energy consumption, and improve transmission efficiency. Finally, to verify the application effect of aerial computing architecture in optimizing the linear algebraic model of communication transmission frequency, this paper compared it with traditional dynamic adjustment models and parallel computational models. The research results showed that for packet 13, the round-trip time required to transmit the model in this article was 1.21&#xa0;ms; the response time was 0.009&#xa0;ms, and the total energy consumption was 89.6-W hours. The traditional dynamic adjustment model required a round-trip time of 4.92&#xa0;ms, a response time of 0.093&#xa0;ms, and a total energy consumption of 119.1-W hours for packet 13 transmissions. The parallel computational model required a round-trip time of 6.33&#xa0;ms, a response time of 0.063&#xa0;ms, and a total energy consumption of 131.4-W hours for packet 13 transmissions. The results showed that the optimized communication transmission frequency linear algebraic model using aerial computing architecture had shorter communication delay and response time, lower energy consumption, and better frequency control performance. This article highlighted the important impact of aerial computing architecture on the stability, real-time performance, and transmission rate of linear algebraic models of communication transmission frequencies, providing more ideas for the design and planning of wireless communication systems.</p>

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Optimization of Communication Transmission Frequency Linear Algebraic Model under Aerial Computing Architecture

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年2月19日

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  • Yufeng Gao

摘要

With the growth of the demand for wireless communication systems, when the existing algebraic model processes real-time data, the frequency adjustment is inflexible, it is difficult to quickly optimize the transmission parameters to cope with network load changes, and the long-term operation fails to effectively control the CPU frequency, resulting in increased energy consumption. To better promote the development of wireless communication systems, this article aimed to use aerial computing architecture to optimize the linear algebraic model of communication transmission frequency, to better meet the needs of today's wireless communication systems. The article first designed a communication frequency stabilization device structure to ensure high stability of the output spectrum. Then it introduced a dynamic frequency adjustment module to achieve real-time adjustment of communication transmission frequency. It then improved the data transmission rate through the design of the aerial computing perception module. This article used a frequency optimization algorithm based on linear algebra to adjust its linear relationship, optimize transmission energy consumption, and improve transmission efficiency. Finally, to verify the application effect of aerial computing architecture in optimizing the linear algebraic model of communication transmission frequency, this paper compared it with traditional dynamic adjustment models and parallel computational models. The research results showed that for packet 13, the round-trip time required to transmit the model in this article was 1.21 ms; the response time was 0.009 ms, and the total energy consumption was 89.6-W hours. The traditional dynamic adjustment model required a round-trip time of 4.92 ms, a response time of 0.093 ms, and a total energy consumption of 119.1-W hours for packet 13 transmissions. The parallel computational model required a round-trip time of 6.33 ms, a response time of 0.063 ms, and a total energy consumption of 131.4-W hours for packet 13 transmissions. The results showed that the optimized communication transmission frequency linear algebraic model using aerial computing architecture had shorter communication delay and response time, lower energy consumption, and better frequency control performance. This article highlighted the important impact of aerial computing architecture on the stability, real-time performance, and transmission rate of linear algebraic models of communication transmission frequencies, providing more ideas for the design and planning of wireless communication systems.