摘要
针对已有单节点频谱感知精度低、协作频谱感知数据融合过程复杂,以及对噪声不确定性敏感等问题,提出一种基于压缩感知与熵的多天线宽带频谱检测算法。算法利用多天线技术,通过共享各阵元接收信号之间的相关信息,建立多任务学习模型,实现阵元之间的协作感知,进而提高单节点感知精度;基于贝叶斯概率模型,解决测量不确定性问题,可提高宽带信号重构精度;并利用频域信息熵进行频谱判决,能够改善算法的噪声鲁棒性。仿真结果表明,在测量数据较少场景下,与匹配追踪和单任务贝叶斯算法相比,信号重构精度较高;且在低信噪比条件下,与已有算法相比,具有较好的检测性能。
Focusing on the problems that accuracy is low in existing single node spectrum sensing with,data fusion process is complicated in cooperative spectrum sensing,and the spectrums are sensitive to noisy uncertainty,we proposed a compressed sensing and entropy based multi-antenna wideband spectrum sensing algorithm.The we utilized multi-antenna technology to establish a multi-task learning model by sharing the correlation information between the received signals of each array element,which can realize the cooperative detection between the array elements,thereby improving the single-node spectrum sensing accuracy.Based on Bayesian probability model,the wideband signal was exactly reconstructed,the goal of reducing the uncertainty measurements was implemented,and the frequency domain information entropy was applied to perform the spectrum decision,which can also improve the noise robustness of the algorithm.The simulation results demonstrate the superior reconstruction accuracy with respect to match pursuit algorithm and single-task Bayesian algorithm in the case of less measurement data.Compared with the benchmark schemes,the proposed algorithm has better detection performance even when under low SNR conditions.
作者
刘春玲
刘敏提
丁元明
杨阳
LIU Chun-ling;LIU Min-ti;DING Yuan-ming;YANG Yang(College of Information Engineering,Dalian University,Dalian Liaoning 116622,China;Key Laboratory of Communication and Network,Dalian University,Dalian Liaoning 116622,China)
出处
《计算机仿真》
北大核心
2020年第11期167-172,共6页
Computer Simulation
基金
装发部领域基金(一般项目),无人机集群自适应组网技术(61403110308)。
关键词
宽带频谱检测
压缩感知
信息熵
多天线
Wideband spectrum detection
Compressed sensing
Information entropy
Multi-antenna
