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基于循环神经网络的SPMA协议信道状态智能检测改进算法

张彦晖 吕娜 缪竞成 高旗 王翔 陈卓

张彦晖,吕娜,缪竞成,等. 基于循环神经网络的SPMA协议信道状态智能检测改进算法[J]. 北京航空航天大学学报,2023,49(3):735-744 doi: 10.13700/j.bh.1001-5965.2021.0309
引用本文: 张彦晖,吕娜,缪竞成,等. 基于循环神经网络的SPMA协议信道状态智能检测改进算法[J]. 北京航空航天大学学报,2023,49(3):735-744 doi: 10.13700/j.bh.1001-5965.2021.0309
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
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

基于循环神经网络的SPMA协议信道状态智能检测改进算法

doi: 10.13700/j.bh.1001-5965.2021.0309
基金项目: 国家自然科学基金(61703427); 陕西省自然科学基金(2020JQ-483)
详细信息
    通讯作者:

    E-mail:1105406986@qq.com

  • 中图分类号: TN929.5;V243

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

Funds: National Natural Science Foundation of China (61703427); Natural Science Foundation of Shaanxi Province (2020JQ-483)
More Information
  • 摘要:

    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。

     

  • 图 1  SPMA协议信道接入控制流程

    Figure 1.  Flow chart of SPMA protocol channel access control

    图 2  不同样本下信道状态检测算法误差

    Figure 2.  Error of channel state detection algorithm under different samples

    图 3  改进后SPMA协议信道接入控制流程

    Figure 3.  Flow chart of improved SPMA protocol channel access control

    图 4  门控循环单元结构

    Figure 4.  Structure diagram of gated recurrent unit

    图 5  流量预测神经网络结构

    Figure 5.  Structure diagram of traffic prediction neural network

    图 6  循环神经网络特征学习过程

    Figure 6.  Feature learning process of recurrent neural network

    图 7  流量预测模型的训练和预测过程

    Figure 7.  Training and prediction process of traffic prediction model

    图 8  传统算法的检测结果

    Figure 8.  Detection results of traditional algorithms

    图 9  改进算法的检测结果

    Figure 9.  Detection results of improved algorithms

    图 10  两种算法检测的流量脉冲及信道忙闲状态与真实数据的对比

    Figure 10.  Comparison of traffic pulses and channel busy/idle status detected by two algorithms with real data

    表  1  两种算法检测准确性对比

    Table  1.   Comparison of detection accuracy of two algorithms

    算法MSERMSEMAER2
    传统算法931.5330.5213.710.67
    改进算法313.5617.707.780.89
    下载: 导出CSV

    表  2  两种算法在不同类型流量下对低中高优先级消息的忙闲状态误判率

    Table  2.   Misjudgment probability of busy and idle status of two algorithms for low, medium and high priority messages under different types of traffic

    流量类型所用算法低优先级消
    息误判率/%
    中优先级消
    息误判率/%
    高优先级消
    息误判率/%
    突发流量
    [100,120]
    传统算法2000
    改进算法500
    稳定流量
    [200,400]
    传统算法000
    改进算法000
    强波动流量
    [400,600]
    传统算法22.6125.5513.76
    改进算法7.3812.288.85
    下载: 导出CSV

    表  3  两种算法时延对比

    Table  3.   Time Delay comparison of two algorithms

    算法1000次时延/ms平均单次时延/ms
    传统算法4.24
    0.004 2
    改进算法48.30
    0.048 3
    下载: 导出CSV
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出版历程
  • 收稿日期:  2021-06-08
  • 录用日期:  2021-08-29
  • 网络出版日期:  2021-09-14
  • 整期出版日期:  2023-03-30

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