基于大数据分析的频谱资源管控系统设计
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摘要
针对电磁环境恶化,频谱资源匮乏、分配不均及其在空间、时间和频域上存在的高度相关性的问题,文中提出了一种基于大数据分析的频谱资源管控系统。该系统由频谱监测与存储、数据分析处理和管控中心3个功能模块组成。首先,采用分布式的数据采集架构采集频谱信息和GPS信息;然后,使用大数据分析技术预测下一次时刻频谱资源的使用情况,以合理的检测可用信道、提高频谱监测的效率,优化频谱监测的策略;最后,使用管控中心管理不同等级用户的频谱使用权限。仿真测试结果表明,该系统能有效地处理海量的频谱数据、提高频谱资源的利用率。
In view of the lack of spectrum resources,the deterioration of the electromagnetic environment,the uneven distribution and the high degree of correlation in space,time and frequency domain,a spectrum resource management and control system based on big data analysis is proposed. The system consists of spectrum monitoring and storage,data analysis and management and control center composed of three functional modules. Firstly,the distributed data acquisition framework is used to collect spectrum information and GPS information. Then,the use of big data analysis technology to predict the next time the use of spectrum resources to detect available channels to improve the efficiency of spectrum monitoring and optimize the spectrum monitoring strategy; finally,use the control center to manage spectrum usage rights of different levels of users. The simulation results show that the system can effectively deal with the massive spectrum data and improve the utilization of spectrum resources.
In view of the lack of spectrum resources,the deterioration of the electromagnetic environment,the uneven distribution and the high degree of correlation in space,time and frequency domain,a spectrum resource management and control system based on big data analysis is proposed. The system consists of spectrum monitoring and storage,data analysis and management and control center composed of three functional modules. Firstly,the distributed data acquisition framework is used to collect spectrum information and GPS information. Then,the use of big data analysis technology to predict the next time the use of spectrum resources to detect available channels to improve the efficiency of spectrum monitoring and optimize the spectrum monitoring strategy; finally,use the control center to manage spectrum usage rights of different levels of users. The simulation results show that the system can effectively deal with the massive spectrum data and improve the utilization of spectrum resources.
引文
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[2]李伊.基于认知无线传感器网络的能效优化频谱检测机制研究[J].湖南邮电职业技术学院学报,2016,15(1):10-14.
[3]于宏毅,程标,胡赟鹏,等.基于空域稀疏性的自适应频谱检测算法[J].电子与信息学报,2016,38(7):1703-1709.
[4]龙敏,吴靓.认知无线电网络中安全的协作频谱检测算法[J].计算机工程与科学,2016,38(10):2045-2050.
[5]李彤,苗成林,吕军.基于功率谱对消的时域相关认知跳频谱检测算法[J].兵工学报,2017,38(9):1754-1760.
[6]赵季红,张艳平,曲桦,等.冲击噪声背景下基于KPCA和C-SVM的频谱检测算法[J].电视技术,2017,41(2):65-69.
[7]马彬,包小敏,谢显中.认知无线网络中非理想频谱检测的切换[J].北京邮电大学学报,2016,39(4):13-18.
[8]王赞,许晓荣,姚英彪.基于能量有效性的贝叶斯压缩感知宽带频谱检测[J].计算机工程,2016,42(3):125-129.
[9]同诗扬,祁广云,衣淑娟.基于农业物联网的改进型双门限频谱检测算法研究[J].农机化研究,2016(1):53-56.
[10]杜红,富爽,史国军,等.认知无线电中双阶段频谱检测算法研究[J].电视技术,2016,40(10):67-70.
[11]吴迪,李智.基于1-bit压缩采样的快速频谱检测方法[J].广东通信技术,2016(1):36-40.
[12]李欣.基于嵌入式平台的微型频谱监测仪器软件设计及实现[D].成都:电子科技大学,2016.
[13]王继业,赵东艳,原义栋,等.电力无线专网中基于频谱检测的SDR技术[J].电子技术应用,2017,43(4):102-106.
[14]李敏乐,毕大平,韩佳辉.基于形态学运算的子带频谱检测算法[J].探测与控制学报,2017,39(4):49-53.
[15]夏伟,邱斌,丁风海,等.基于Lab VIEW的频谱监测系统设计与实现[J].计算机测量与控制,2016,24(12):24-27.