基于循环神经网络的SPMA协议信道状态智能检测改进算法

为实现对时敏目标的快速探测、定位和打击，战术瞄准网络技术(TTNT)对战术信息接入信道、交互传输的实时性、可靠性提出高要求。TTNT采用基于统计优先的多址接入(SPMA)协议，通过周期性计算统计平均的思想，估计当前信道状态，控制战术信息接入信道的时机。该思想仅适用于流量相对平稳的情况，在流量非平稳时会导致较大的信道状态检测误差。针对此问题，引入流量预测技术，提出基于循环神经网络的SPMA协议信道状态智能检测改进算法。利用循环神经网络的学习特点学习历史流量数据的隐含特征，构建流量预测器对瞬时时刻的流量脉冲到达数进行实时预测，从而准确获取当前信道状态。实验结果表明：所提算法对信道状态的检测结果更接近真实值，显著降低了信道忙闲状态的误判率。

Improved intelligent detection algorithm for SPMA protocol channel state based on recurrent neural network

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.