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小样本学习驱动的无线频谱状态感知

申滨 李月 王欣 王紫昕

申滨, 李月, 王欣, 王紫昕. 小样本学习驱动的无线频谱状态感知[J]. 电子与信息学报, 2024, 46(4): 1231-1239. doi: 10.11999/JEIT230377
引用本文: 申滨, 李月, 王欣, 王紫昕. 小样本学习驱动的无线频谱状态感知[J]. 电子与信息学报, 2024, 46(4): 1231-1239. doi: 10.11999/JEIT230377
SHEN Bin, LI Yue, WANG Xin, WANG Zixin. Wireless Spectrum Status Sensing Driven by Few-Shot Learning[J]. Journal of Electronics & Information Technology, 2024, 46(4): 1231-1239. doi: 10.11999/JEIT230377
Citation: SHEN Bin, LI Yue, WANG Xin, WANG Zixin. Wireless Spectrum Status Sensing Driven by Few-Shot Learning[J]. Journal of Electronics & Information Technology, 2024, 46(4): 1231-1239. doi: 10.11999/JEIT230377

小样本学习驱动的无线频谱状态感知

doi: 10.11999/JEIT230377
基金项目: 国家自然科学基金(62371082)
详细信息
    作者简介:

    申滨:男,教授,研究方向为大规模MIMO系统、认知无线电

    李月:女,研究生,研究方向为认知无线电

    王欣:女,博士生,研究方向为认知无线电

    王紫昕:女,硕士生,研究方向为认知无线电

    通讯作者:

    申滨 shenbin@cqupt.edu.cn

  • 中图分类号: TN911.73

Wireless Spectrum Status Sensing Driven by Few-Shot Learning

Funds: The National Nature Science Foundation of China (62371082)
  • 摘要: 无线频谱状态感知是实现无线频谱资源高效利用及各种用频系统和谐共存的先决条件之一。针对复杂无线传播环境下获取的频谱观测往往存在数据稀疏性、数据类别分布不稳定、标记数据严重不足的情况,该文提出基于插值和小样本学习(FSL)分类的无线频谱状态感知方法。首先,对捕获的稀疏频谱观测数据插值,构建频谱状态地图,作为频谱状态分类器的输入数据。其次,针对频谱数据类别分布不稳定、数据量严重不足的问题,基于小样本学习方法,利用嵌入模块和度量模块协同工作,以实现快速精确的频谱状态分类。具体地,利用嵌入模块将频谱数据映射到嵌入空间,提取频谱数据中的隐含特征;在度量模块的设计中,分别提出基于原型和基于样例的两种类别表示方式,通过计算待分类样本与类别之间的相似度判断待分类样本类别。最后,为了确保分类模型克服测试样本数量少导致过拟合问题,设置A-way B-shot任务训练模型。仿真结果表明,与传统机器学习方法相比,本文模型可以在低信噪比条件下进行精准分类;同时,在测试集样本数很少的情况下,或者在测试集中出现在训练集从未见到的新类时,所训练的模型也可以精准快速判别无线频谱的场景类别。
  • 图  1  总体方案框架

    图  2  不同数据充分度条件下补全的频谱状态地图

    图  3  传统ML方法与FSL方法的频谱感知性能对比

    图  4  不同数据充分度下,SVM、原型法、样例法在两种插值算法下的频谱感知性能对比

    图  5  不同插值算法下SVM、原型法、样例法的频谱状态感知性能对比

    图  6  不同特征提取网络的频谱感知性能对比

    图  7  A-way B-shot任务中不同A值和B值的频谱状态感知性能对比

    1  基于原型的度量学习训练集损失计算

     输入:$ {S_{{\mathrm{tr}}}} $, $ {Q_{{\mathrm{tr}}}} $,$ \varphi $
     输出:随机生成的训练episode的损失$ J $
     (1) $ {u_i} \in {\mathrm{Random}}\left( {\left[ {0,1, \cdots ,{2^N} - 1} \right],{N_{{\mathrm{tr}}}}} \right) $
     (2) For i in $ \left\{ {1,2, \cdots ,{N_{{\mathrm{tr}}}}} \right\} $ do
     (3)  $ {c_{{N_{{\mathrm{tr}}}}}} \leftarrow \dfrac{1}{{\left| {{S_{{\mathrm{tr}}}}} \right|}}\displaystyle\sum\limits_{\left( {{{\boldsymbol{Y}}_i},{u_i}} \right) \in {S_{\rm{tr}}}} {{f_\varphi }} \left( {{{\boldsymbol{Y}}_i}} \right) $
     (4) End for
     (5) $ J \leftarrow 0 $
     (6) For i in $ \left\{ {1,2, \cdots ,{N_{{\mathrm{tr}}}}} \right\} $ do
     (7)  For $ \left( {{{\boldsymbol{Y}}^ * },u} \right) $ in $ {Q_{{\mathrm{tr}}}} $ do
     $ (8) \;\;{p_\varphi }\left( {u = {u_{{N_{{\mathrm{tr}}}}}}\mid {{\boldsymbol{Y}}^ * }} \right) = \dfrac{{{\text{exp}}\left( { - d\left( {{f_\varphi }\left( {{{\boldsymbol{Y}}^ * }} \right),{c_{{N_{\rm{tr}}}}}} \right)} \right)}}{{\displaystyle\sum\limits_{{{N'}_{{\mathrm{tr}}}}} {{\text{exp}}\left( { - d\left( {{f_\varphi }\left( {{{\boldsymbol{Y}}^ * }} \right),{{c}'_{{N_{{\mathrm{tr}}}}}}} \right)} \right)} }} $
     (9)   $ J\left( \varphi \right) = - {\text{ln}}\:{p_\varphi }\left( {u = {u_{{N_{{\mathrm{tr}}}}}}\mid {{\boldsymbol{Y}}^ * }} \right) $
     (10) $ J \leftarrow J + \dfrac{1}{{{N_{\mathrm{s}}}{N_{\mathrm{q}}}}}J\left( \varphi \right) $
     (11) End for
     (12) End for
    下载: 导出CSV

    2  基于样例的度量学习训练集损失计算

     输入:$ {S_{{\mathrm{tr}}}} $, $ {Q_{{\mathrm{tr}}}} $, $ \varphi $, $ \psi $
     输出:随机生成的训练episode的损失$ J $
     (1) $ {\mathcal{Y}^*} \leftarrow {g_\psi }({{\boldsymbol{Y}}^*}) $,$ {{\boldsymbol{Y}}^ * } $ in $ {Q_{{\mathrm{tr}}}} $
     (2) $ \mathcal{Y} \leftarrow {g_\psi }({\boldsymbol{Y}}) $,$ {\boldsymbol{Y}} $ in $ {S_{{\mathrm{tr}}}} $
     (3) $ J \leftarrow 0 $
     (4) For i in $ \left\{ {1,2, \cdots ,{N_{{\mathrm{tr}}}}} \right\} $ do
     (5)  For $ \left( {{{\boldsymbol{Y}}^ * },u} \right) $ in $ {Q_{{\mathrm{tr}}}} $ do
     (6)   $ {p_\varphi }\left( {u = {u_{{N_{{\mathrm{tr}}}}}}\mid {{\boldsymbol{Y}}^ * }} \right) = \displaystyle\sum\limits_{i = 1}^{{N_d}} {\displaystyle\sum\limits_{j = 1}^{{N_k}} } \cos\left( {\mathcal{Y}_i^*,\mathcal{Y}_i^j} \right) $
     (7)   $ J\left( \varphi \right) = - {\text{ln}}\:{p_\varphi }\left( {u = {u_{{N_{{\mathrm{tr}}}}}}\mid {{\boldsymbol{Y}}^ * }} \right) $
     (8)   $ J \leftarrow J + \dfrac{1}{{{N_{\mathrm{s}}}{N_{\mathrm{q}}}}}J\left( \varphi \right) $
     (9) End for
     (10) End for
    下载: 导出CSV

    表  1  IDW插值法与Kriging插值法RMSE性能比较

    $ \rho $RMSE
    IDWKriging
    0.250.052 20.066 5
    0.200.060 20.080 5
    0.150.060 50.082 5
    0.100.081 20.106 5
    0.050.092 30.125 5
    下载: 导出CSV

    表  2  SVM算法与FSL算法在5-way B-shot条件下的准确度比较

    算法名称5-way 1-shot5-way 3-shot5-way 5-shot
    SVM0.200 00.220 00.240 0
    基于原型0.866 40.898 50.901 4
    基于样例0.956 40.975 840.997 8
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-05-05
  • 修回日期:  2024-02-01
  • 网络出版日期:  2024-02-16
  • 刊出日期:  2024-04-24

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