基于能量有效性的贝叶斯压缩感知宽带频谱检测
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摘要
在认知无线网络(CRN)中,基于压缩感知的宽带频谱检测仅关注频谱有效性,未考虑到频谱检测过程中节点的能效问题,在提高频谱检测性能的同时造成节点能耗开销过大。为此,提出一种在保障节点能量有效性基础上,进行基于贝叶斯压缩感知(BCS)稀疏重构的CRN宽带频谱检测方法。推导感知能耗解析式,构造节点感知能耗模型,在满足宽带压缩频谱检测概率和BCS重构均方误差(MSE)阈值的约束条件下,通过改变BCS测量矩阵中采样点数实现感知能耗最小化。仿真结果表明,当虚警概率为0.04时,在采样点数较小的情况下,该方法的检测概率高于感知-能耗折衷方法。在重构MSE小于15 d B的条件下,与正交匹配追踪方法的重构能耗相比,基于BCS的节点重构能耗明显下降。
In Cognitive Radio Network( CRN),wideband spectrum detection based on Compressive Sensing( CS) only focuses on spectral efficiency. Energy efficiency is hardly considered in spectrum detection phase,which results in larger energy consumption with the improvement of spectrum detection performance. Hence,CRN wideband spectrum detection method based on Bayesian Compressive Sensing( BCS) sparse reconstruction with the guarantee of energy efficiency is proposed. Sensing energy analytical formula is derived and the corresponding energy consumption model is constructed.The minimization of sensing energy consumption can be achieved via the change of sample numbers in BCS measurement matrix,with the constraint conditions of wideband compressive spectrum detection probability and BCS reconstruction Mean Square Error( MSE) thresholds. Simulation result indicates that,when false alarm probability is 0. 04,the detection probability of the proposed method outperforms sensing-energy tradeoff approach obviously in fewer sample numbers scenario. Moreover,when reconstruction MSE is lower than 15 d B,compared with Orthogonal Matching Pursuit( OMP)method,reconstruction energy consumption based on BCS can be reduced obviously.
In Cognitive Radio Network( CRN),wideband spectrum detection based on Compressive Sensing( CS) only focuses on spectral efficiency. Energy efficiency is hardly considered in spectrum detection phase,which results in larger energy consumption with the improvement of spectrum detection performance. Hence,CRN wideband spectrum detection method based on Bayesian Compressive Sensing( BCS) sparse reconstruction with the guarantee of energy efficiency is proposed. Sensing energy analytical formula is derived and the corresponding energy consumption model is constructed.The minimization of sensing energy consumption can be achieved via the change of sample numbers in BCS measurement matrix,with the constraint conditions of wideband compressive spectrum detection probability and BCS reconstruction Mean Square Error( MSE) thresholds. Simulation result indicates that,when false alarm probability is 0. 04,the detection probability of the proposed method outperforms sensing-energy tradeoff approach obviously in fewer sample numbers scenario. Moreover,when reconstruction MSE is lower than 15 d B,compared with Orthogonal Matching Pursuit( OMP)method,reconstruction energy consumption based on BCS can be reduced obviously.
引文
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[2]Donoho D L.Compressive Sensing[J].IEEE Transactions on Information Theory,2006,52(4):1289-1306.
[3]焦李成,杨淑媛,刘芳,等.压缩感知回顾与展望[J].电子学报,2011,39(7):1651-1662.
[4]Zeng Fanzi,Li Chen,Tian Zhi.Distributed Compressive Spectrum Sensing in Cooperative Multihop Cognitive Networks[J].IEEE Journal of Selected Topics in Signal Processing,2011,5(1):37-48.
[5]Tian Zhi.Compressed Wideband Sensing in Cooperative Cognitive Radio Networks[C]//Proceedings of IEEE GLOBECOM’08.Washington D.C.,USA:IEEE Press,2008:1-5.
[6]Liu Dandan,Wang Wenbo,Guo Wenbin.Green Coopera-tive Spectrum Sharing Communication[J].IEEE Communications Letters,2013,17(3):459-462.
[7]赵春晖,许云龙.能量约束贝叶斯压缩感知检测算法[J].通信学报,2012,33(10):1-6.
[8]唐亮,周正,石磊,等.基于能量均衡的无线传感器网络压缩感知算法[J].电子与信息学报,2011,33(8):1919-1923.
[9]Xu Xiaorong,Huang Aiping,Bao Jianrong.Energy Efficiency Analysis of Cooperative Sensing and Sharing in Cognitive Radio Networks[C]//Proceedings of IEEE ISCIT’11.Washington D.C.,USA:IEEE Press,2011:422-427.
[10]Huang Shiwei,Chen Hongbin,Zhang Yan,et al.Sensingenergy Tradeoff in Cognitive Radio Networks with Relays[J].IEEE Systems Journal,2013,7(1):68-76.
[11]Ji Shihao,Xue Ya,Carin L.Bayesian Compressive Sensing[J].IEEE Transactions on Signal Processing,2008,56(6):2346-2356.
[12]Michael E T.Sparse Bayesian Learning and the Relevance Vector Machine[J].Journal of Machine Learning Research,2001,1(1):211-244.
[13]Xiang Ling,Wu Bin,Wen Hong,et al.Adaptive Threshold Control for Energy Detection Based Spectrum Sensing in Cognitive Radios[J].IEEE Wireless Communications Letters,2012,1(5):448-451.
[14]Feng Daquan,Jiang Chenzi,Lim G,et al.A Survey of Energy-efficient Wireless Communications[J].IEEE Communications Surveys&Tutorials,2013,15(1):167-178.
[15]An Chunyan,Ji Hong,Li Yi.Energy-efficient Collaborative Scheme for Compressed Sensing Based Spectrum Detection in Cognitive Radio Networks[C]//Pro-ceedings of IEEE WCNC’12.Washington D.C.,USA:IEEE Press,2012:1360-1364.