| Citation: | ZHANG Y H，LYU N，MIAO J C，et al. Improved intelligent detection algorithm for SPMA protocol channel state based on recurrent neural network[J]. Journal of Beijing University of Aeronautics and Astronautics，2023，49（3）：735-744 （in Chinese） doi: 10.13700/j.bh.1001-5965.2021.0309
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By connecting sensors and shooters, tactical target network technology (TTNT) can realize rapid detection, positioning and strike of time-sensitive targets in denial environments. To that end, real-time and reliable channel access is highly required for tactical information transmission. TTNT uses the statistical priority-based multiple access (SPMA) protocol, which periodically calculates the statistical average number of arrival traffic pulses, to estimate the current channel state and thus control the timing of tactical information access. However, methods based on statistical average are merely suitable for stationary traffic, and will lead to large error in channel state detection when the traffic is non-stationary. To solve this problem, the traffic prediction technology was adopted and an improved detection algorithm for SPMA protocol channel state based on recurrent neural network was proposed. Meanwhile, in order to accurately obtain the current channel state, the recurrent neural network was employed to learn the hidden characteristics of historical traffic data, and a traffic predictor was constructed to timely predict the number of traffic pulses arriving at an instant. Experiments showed that the results of communication state detection with our algorithm is more realistic, which can significantly reduce the false judgment rate of the channel state.
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