基于機器學習主用戶發(fā)射模式分類的蜂窩認知無線電網(wǎng)絡頻譜感知
doi: 10.11999/JEIT191012
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重慶郵電大學通信與信息工程學院 重慶 400065
Machine Learning Based Primary User Transmit Mode Classification for Spectrum Sensing in Cellular Cognitive Radio Network
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School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
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摘要:
近年來，基于機器學習(ML)的頻譜感知技術(shù)為認知無線電系統(tǒng)提供了新型的頻譜狀態(tài)監(jiān)測解決方案。利用蜂窩認知無線電網(wǎng)絡(CCRN)中的次級用戶設備(SUE)所能提供的大量頻譜觀測數(shù)據(jù)，該文提出了一種基于主用戶(PU)傳輸模式分類的頻譜感知方案。首先，基于多種典型的ML算法，對于網(wǎng)絡中的多個主用戶發(fā)射機(PUT)的傳輸模式進行分類辨識，在網(wǎng)絡整體層面上確定所有PUT的聯(lián)合工作狀態(tài)。然后，網(wǎng)絡中的SUE根據(jù)其所處地理位置或者頻譜觀測數(shù)據(jù)，判斷其在當前已判定的PUT發(fā)射模式下接入授權(quán)頻譜的可能性。由于PUT在網(wǎng)絡中的實際位置可能事先已知或者無法提前確定，該文給出了3種不同的處理方法。理論推導與實驗結(jié)果表明，所提方案與傳統(tǒng)的能量檢測方案相比，不僅改善了頻譜感知性能，還增加了蜂窩認知網(wǎng)絡對于授權(quán)頻譜的動態(tài)訪問機會。該方案可以作為蜂窩認知無線電網(wǎng)絡中的一種高效實用的頻譜感知解決方案。
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關(guān)鍵詞:
- 蜂窩認知無線電網(wǎng)絡 /
- 機器學習 /
- 頻譜感知 /
- 支持向量機 /
- 卷積神經(jīng)網(wǎng)絡
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模式分類 輸入：${{{Y}}_m},\widehat {{Y}},G,$閾值$\varphi $ 輸出：${\hat {{S}}^{(m)} }$ 初始化 (1) ${{{y}}_{m,1} } = {\rm{vec} }({{{Y}}_m})$%矩陣轉(zhuǎn)化為列向量 (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) 獲取行位置索引向量 ${{x}}{\rm{ = \{ } }{x_1}{\rm{,} }{x_2}{\rm{,} } ··· {\rm{,} }{x_Q}{\rm{\} } }$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) ${l_{ {\rm{opt} } } } = \mathop {\arg \min }\limits_{l = 1,2, \cdots ,{2^N} - 1} {\varDelta _l}$ 輸出：${\hat {{S}}^{(m)} } = {\mathbb{S}_{ {l_{ {\rm{opt} } } } } }$ 注：${\rm{|} }{{{h}}_1}{\rm{|} }$為集合${{{h}}_1}$的勢，即其所包含的所有元素的個數(shù)。 
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表 1 CNN分類算法采用的結(jié)構(gòu)參數(shù)
層類型 輸入尺寸 濾波器尺寸 激活函數(shù) 卷積層(1) 120×120 3×3×32 ReLu 卷積層(2) 60×60 3×3×64 ReLu 全連接層 14400×1 1024個神經(jīng)元 Softmax
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表 2 PUT傳輸功率為32 dBm時，PUT傳輸模式分類準確率
算法名稱 數(shù)據(jù)充分性條件 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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