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基于深度學(xué)習(xí)的動態(tài)主用戶頻譜感知算法
電子技術(shù)應(yīng)用
李新玉1,趙知勁1,,2
1.杭州電子科技大學(xué) 通信工程學(xué)院,,浙江 杭州 310018; 2.中國電子科技集團第36研究所 通信系統(tǒng)信息控制技術(shù)國家級重點實驗室,,浙江 嘉興 314001
摘要: 實際的頻譜感知場景中主用戶可能隨機到達或者離開,當(dāng)主用戶狀態(tài)在實時頻譜感知期間動態(tài)變化時,,現(xiàn)有的靜態(tài)頻譜感知算法性能急劇惡化,。針對該現(xiàn)狀,研究提出基于殘差收縮注意力機制的動態(tài)主用戶頻譜感知算法,。頻譜感知間隔內(nèi),,主用戶隨機到達或者隨機離開的時間服從均勻分布,。采用深度殘差收縮網(wǎng)絡(luò)(DRSN)提取動態(tài)主用戶特征,并且濾除冗余的噪聲特征,;利用協(xié)調(diào)注意力模塊(CAM)增強每個通道不同方向的特征信息,,提高模型對動態(tài)主用戶特征的表達能力。仿真結(jié)果表明,,所提算法性能優(yōu)于對比算法ResNet,、CBAM_IQ和CBAM_Energy,所提算法對主用戶隨機到達或者離開服從不同分布的主用戶都可以保持較高的檢測概率,。
中圖分類號:TN925 文獻標(biāo)志碼:A DOI: 10.16157/j.issn.0258-7998.234333
中文引用格式: 李新玉,,趙知勁. 基于深度學(xué)習(xí)的動態(tài)主用戶頻譜感知算法[J]. 電子技術(shù)應(yīng)用,2024,,50(1):60-65.
英文引用格式: Li Xinyu,,Zhao Zhijin. Dynamic primary user spectrum sensing algorithm based on deep learning[J]. Application of Electronic Technique,2024,,50(1):60-65.
Dynamic primary user spectrum sensing algorithm based on deep learning
Li Xinyu1,,Zhao Zhijin1,2
1.School of Communication Engineering,, Hangzhou Dianzi University,, Hangzhou 310018, China,; 2.National Key Laboratory of Communication System Information Control Technology,, 36th Research Institute of China Electronics Technology Group, Jiaxing 314001,, China
Abstract: In actual spectrum sensing scenarios, the primary user may arrive or leave randomly, and when the primary user state changes dynamically during real-time spectrum sensing, the performance of the existing static spectrum sensing algorithm deteriorates sharply. For this situation, this paper propose a dynamic primary user spectrum sensing algorithm based on the residual shrinkage and attention mechanism. During the spectrum-sensing interval, the time when the primary user randomly arrives or leaves randomly follows a uniform distribution. The “deep residual shrinkage network (DRSN)” is used to extract dynamic primary user features and filter out redundant noise features. The “coordination attention module (CAM)” is used to improve the ability of the model to express the features of the dynamic primary user. Simulation results show that the proposed algorithm performs are better than ResNet algorithm, CBAM_IQ algorithm and CBAM_Energy algorithm. The proposed algorithm can maintain a high detection probability for the primary users who randomly arrive or leave following different distributions.
Key words : cognitive radio,;spectrum sensing;dynamic primary user,;deep residual contraction network,;coordinated attention mechanism

引言

隨著5G通信技術(shù)的發(fā)展和無線通信業(yè)務(wù)的飛速增長,頻譜資源處于供不應(yīng)求的狀態(tài),。認(rèn)知無線電(Cognitive Radio, CR)[1]的提出緩解了頻譜資源緊張的局面,,頻譜感知(Spectrum Sensing, SS)[2]是認(rèn)知無線電的關(guān)鍵技術(shù),它允許次用戶(Secondary User, SU)使用空閑的授權(quán)頻譜,。靜態(tài)主用戶(Primary User ,PU)的頻譜感知算法已經(jīng)得到深入研究,,靜態(tài)主用戶是指感知階段主用戶狀態(tài)保持不變,即始終活躍或者始終沉默,,而實際場景中,,感知過程中主用戶可能隨機到達或者隨機離開。當(dāng)主用戶狀態(tài)發(fā)生變化時,,頻譜感知算法性能會受到影響,。因此,研究在感知期間主用戶的狀態(tài)發(fā)生變化的頻譜感知算法具有很強的實際意義,。


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作者信息:

李新玉1,,趙知勁1,2

(1.杭州電子科技大學(xué) 通信工程學(xué)院,,浙江 杭州 310018,;
2.中國電子科技集團第36研究所 通信系統(tǒng)信息控制技術(shù)國家級重點實驗室,浙江 嘉興 314001)


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