Machine Learning Based Primary User Transmit Mode Classification for Spectrum Sensing in Cellular Cognitive Radio Network
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摘要:
近年来，基于机器学习(ML)的频谱感知技术为认知无线电系统提供了新型的频谱状态监测解决方案。利用蜂窝认知无线电网络(CCRN)中的次级用户设备(SUE)所能提供的大量频谱观测数据，该文提出了一种基于主用户(PU)传输模式分类的频谱感知方案。首先，基于多种典型的ML算法，对于网络中的多个主用户发射机(PUT)的传输模式进行分类辨识，在网络整体层面上确定所有PUT的联合工作状态。然后，网络中的SUE根据其所处地理位置或者频谱观测数据，判断其在当前已判定的PUT发射模式下接入授权频谱的可能性。由于PUT在网络中的实际位置可能事先已知或者无法提前确定，该文给出了3种不同的处理方法。理论推导与实验结果表明，所提方案与传统的能量检测方案相比，不仅改善了频谱感知性能，还增加了蜂窝认知网络对于授权频谱的动态访问机会。该方案可以作为蜂窝认知无线电网络中的一种高效实用的频谱感知解决方案。
Abstract:In recent years, Machine Learning (ML) based spectrum sensing technology has provided a new solution in spectrum status identification for cognitive radio systems. Based on the large amount of spectrum observations captured by the Secondary User Equipment (SUE) in the Cellular Cognitive Radio Network (CCRN), this paper proposes a spectrum sensing scheme based on the Primary User (PU) transmission mode classification. Firstly, based on a variety of typical ML classification algorithms, the proposed scheme classifies the transmission mode of multiple Primary User Transmitters (PUTs) in the CCRN, and determines the joint operating state of all the PUTs in the CCRN. Subsequently, the SUE evaluates the possibility of accessing the licensed spectrum in the currently determined PUT transmission mode according to its geographical location or spectrum observation data. Since the actual locations of the PUTs in the network may be readily known in advance or unaware of at all, the proposed scheme solves the problem in three different methods. Theoretical derivation and experimental results show that compared with the traditional energy detection scheme, the proposed scheme not only remarkably improves the spectrum sensing performance, but also significantly increases the opportunities of dynamic accessing to the licensed spectrum for the SUEs. The proposed scheme can be used as an efficient and practical spectrum sensing solution in the CCRN.
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算法1 基于能量值模板差值的PUT模式分类 输入：${{{Y}}_m},\widehat {{Y}},G,$阈值$\varphi $ 输出：${\hat {{S}}^{(m)} }$ 初始化 (1) ${{{y}}_{m,1} } = {\rm{vec} }({{{Y}}_m})$%矩阵转化为列向量 (2) ${{{y}}_{m,2} } = {\rm{sort} }({{{y}}_{m,1} },{\rm{descending} })$%降序排列 (3) ${\rm{ = \{ }}{Z_{{x_1},{y_1}}},{Z_{{x_2},{y_2}}}, \cdots ,{Z_{{x_Q},{y_Q}}}\} $ (4) 获取行位置索引向量 ${{x}}{\rm{ = \{ } }{x_1}{\rm{,} }{x_2}{\rm{,} } ··· {\rm{,} }{x_Q}{\rm{\} } }$及 (5) 列位置索引向量 ${{y}}{\rm{ = \{ } }{y_1}{\rm{,} }{y_2}{\rm{,} } ··· {\rm{,} }{y_Q}{\rm{\} } }$ (6) IF$({Z_{ {x_1},{y_1} } } < \varphi )\& ({Z_{ {x_2},{y_2} } } < \varphi )\& ··· \& ({Z_{ {x_G},{y_G} } } < \varphi )$ (7) ${\hat {{S}}^{(m)} } = {\mathbb{S}_0}$ (8) Else (9) For $i = 1:1:G$ (10) For $j = i + 1:1:G$ (11) If $(\left| {{x_i} - {x_j}} \right| < g)\& (\left| {{y_i} - {y_j}} \right| < g)$ (12) ${Z_{{x_j},{y_j}}} = 0$ (13) EndIF (14) EndFor (15) EndFor (16) EndIF (17) For $i = 1:1:G$ (18) ${{{h}}_{\rm{1} } }(i) = {\rm{find} }({x_i}\left| { {Z_{ {x_i},{y_i} } } \ne 0} \right.)$ (19) ${{{h}}_{\rm{2} } }(i) = {\rm{find} }({y_i}\left| { {Z_{ {x_i},{y_i} } } \ne 0} \right.)$ (20) EndFor (21) ${\varDelta _l} = \displaystyle\sum\limits_{i = 1}^{\left| { {h_1} } \right|} {|{ {{Y} }_m}({ {{h} }_1}(i),{ {{h} }_2}(i)) - { {{Y} }_l}({ {{h} }_1}(i),{ {{h} }_2}(i))|}$ (22) ${l_{ {\rm{opt} } } } = \mathop {\arg \min }\limits_{l = 1,2, \cdots ,{2^N} - 1} {\varDelta _l}$ 输出：${\hat {{S}}^{(m)} } = {\mathbb{S}_{ {l_{ {\rm{opt} } } } } }$ 注：${\rm{|} }{{{h}}_1}{\rm{|} }$为集合${{{h}}_1}$的势，即其所包含的所有元素的个数。 
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表 1 CNN分类算法采用的结构参数
层类型 输入尺寸 滤波器尺寸 激活函数 卷积层(1) 120×120 3×3×32 ReLu 卷积层(2) 60×60 3×3×64 ReLu 全连接层 14400×1 1024个神经元 Softmax
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表 2 PUT传输功率为32 dBm时，PUT传输模式分类准确率
算法名称 数据充分性条件 11.1% 47.4% 100% 能量值模板差值 0.12 0.26 0.28 K-means 0.42 0.43 0.44 HOG+SVM_8*8 0.12 0.16 0.17 HOG+SVM_16*16 0.12 0.12 0.18 CNN 0.62 0.96 0.99
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