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认知车联网中评估频谱稳定性的动态频谱接入算法

马彬 杨祖敏 谢显中

马彬, 杨祖敏, 谢显中. 认知车联网中评估频谱稳定性的动态频谱接入算法[J]. 电子与信息学报, 2025, 47(5): 1474-1485. doi: 10.11999/JEIT240927
引用本文: 马彬, 杨祖敏, 谢显中. 认知车联网中评估频谱稳定性的动态频谱接入算法[J]. 电子与信息学报, 2025, 47(5): 1474-1485. doi: 10.11999/JEIT240927
MA Bin, YANG Zumin, XIE Xianzhong. Dynamic Spectrum Access Algorithm for Evaluating Spectrum Stability in Cognitive Vehicular Networks.[J]. Journal of Electronics & Information Technology, 2025, 47(5): 1474-1485. doi: 10.11999/JEIT240927
Citation: MA Bin, YANG Zumin, XIE Xianzhong. Dynamic Spectrum Access Algorithm for Evaluating Spectrum Stability in Cognitive Vehicular Networks.[J]. Journal of Electronics & Information Technology, 2025, 47(5): 1474-1485. doi: 10.11999/JEIT240927

认知车联网中评估频谱稳定性的动态频谱接入算法

doi: 10.11999/JEIT240927
基金项目: 重庆市教委科学技术研究重大项目(KJZD-M201900602)
详细信息
    作者简介:

    马彬:男,教授,博士生导师,研究方向为异构无线网络,认知无线电网络等

    杨祖敏:男,硕士生,研究方向为认知车联网

    谢显中:男,教授,博士生导师,研究方向为无线和移动通信技术

    通讯作者:

    杨祖敏 cqupt_yzm@163.com

  • 中图分类号: TN915

Dynamic Spectrum Access Algorithm for Evaluating Spectrum Stability in Cognitive Vehicular Networks.

Funds: The Major Project of Science and Technology Research of Chongqing Education Commission (KJZD-M201900602)
  • 摘要: 在带频谱认知的车联网中,由于车辆终端的高动态移动性和无线电环境的复杂性,使频谱的稳定性难以评估。该文提出一种评估频谱稳定性的动态频谱接入算法。首先,基于信噪比、接收信号强度和带宽参数,利用长短期记忆神经网络预测出参数在未来1个周期内多时刻的值,并计算各参数1个周期的变化率,将结果作为频谱稳定性的评估指标。其次,利用K-Means算法对变化率向量进行聚类,构建稳定性评估模型。再次,根据稳定性评估结果重构了状态空间和奖励函数,提出一种基于强化学习的动态频谱接入算法。最后,实验结果表明,所提算法能够满足不同车辆终端业务的稳定性需求,提高频谱资源的利用率,同时降低频谱接入过程中的碰撞概率。
  • 图  1  算法流程图

    图  2  预测模型神经网络结构

    图  3  Q值更新过程及神经网络结构

    图  4  城市认知车联网仿真图

    图  5  带宽训练集预测结果

    图  6  带宽测试集预测结果

    图  7  训练集损失函数曲线

    图  8  测试集准确率曲线

    图  9  频谱利用率对比结果`

    图  10  碰撞概率对比结果

    图  11  累计稳定性指数对比结果

    图  12  累计吞吐量对比结果

    图  13  不同车辆数目与信道数目下的收敛速度对比

    表  1  频谱参数定义表

    变量 描述
    $ P_t^{i,j} $ $t$时刻车辆终端$i$检测到信道$ j $的发射功率
    $ h_t^{i,j} $ $t$时刻车辆终端$i$所接入信道$j$的信道增益
    $ d_t^{i,j} $ $t$时刻车辆终端$i$到信道$j$所属基站间的距离
    $ {\mu _t} $ 均值为0,标准差为$\sigma $的背景噪声
    ${\text{high}}(f_t^{i,j})$ $t$时刻车辆终端$i$所接入信道$j$的最高频率
    ${\text{low}}(f_t^{i,j})$ $t$时刻车辆终端$i$所接入信道$j$的最低频率
    $ T $ 预测时间步长
    $ B_T^{'i,j} $ 信道$ j $在时间步长$ T $内的带宽变化率
    $ \xi _T^{'i,j} $ 信道$ j $在时间步长$ T $内的接收信号强度变化率
    $N$ 车辆终端集合$N = \{ 1,2,\cdots,n\} $
    $M$ 信道集合$M = \{ 1,2,\cdots,m\} $
    下载: 导出CSV

    1  基于强化学习的动态频谱接入算法

     输入:学习率$ \alpha $,折扣因子$ \beta $,探索概率$\varepsilon $,Mini-batch的长度$ L $
     输出:最优Q-Network参数${\theta _t}$
     (1)为每一个车辆终端以随机权重${\theta _t}$的方式初始化Q-Network
     (2) for Iteration $ I $=1, 2, ···, $ i $ do
     (3)   for Time slot $ T $=1, 2, ···, $ t $ do
     (4)    for User $ N $ =1, 2, ···, $ n $ do
     (5)     使用Mini-batch从经验回放池中随机提取$ L $条经验
     (6)     使用经验元组根据式(11)对损失函数进行梯度下降
     (7)     更新神经网络参数
     (8)     车辆终端根据式(12)更新Q值
     (9)     车辆终端根据式(13)选择信道接入
     (10)     车辆终端根据式(9)获得奖励
     (11)   end for
     (12)  for User$ N $ =1, 2, ···, $ n $ do
     (13)  获取下一个状态空间向量${\boldsymbol{s}}_{t + 1}^{N,M}$,进行下一次信道选择
     (14)   end for
     (15)    end for
     (16)     end for
    下载: 导出CSV

    表  2  仿真参数设置

    强化学习训练参数 参数设置
    授权信道数目 2
    车辆终端$n$ 10
    传输高稳定性业务车辆终端 5
    传输低稳定性业务车辆终端 5
    学习率$\alpha $ 0.001
    折扣因子$\beta $ 0.95
    探索概率$\varepsilon $ $1.0 \to 0.1$
    激活函数 ReLu
    优化器 Adam
    Mini-batch大小 4个经验元组
    单次训练次数 5000次循环
    总训练次数 20次
    车辆速度 [10, 15] m/s
    下载: 导出CSV

    表  3  预测模型参数设置

    预测模型参数 参数设置
    学习率 $1.0 \to 0.001$
    损失函数 RMSE
    优化器 Adam
    单次训练次数
    总训练次数
    训练集样本
    测试集样本
    20
    500
    650
    300
    下载: 导出CSV
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出版历程
  • 收稿日期:  2024-10-21
  • 修回日期:  2025-03-03
  • 网络出版日期:  2025-03-14
  • 刊出日期:  2025-05-01

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