摘要
目前,5G无线通信信号弱覆盖区域多采用单层级的识别模型,能够在合理范围内获取识别结果,但是精准度不高,导致误识率增大,因此提出对基于随机森林的5G无线通信信号弱覆盖区域识别方法的分析与研究。首先,对信号识别的需求和标准进行数据采集与识别环境预处理,设定定向识别节点;其次,综合随机森林技术,构建多层级的信号识别结构,提升识别结果的精准度;最后,构建随机森林通信信号识别模型,采用多阶信号集成处理实现区域识别。测试结果表明,本文所设计的随机森林通信信号弱覆盖区域识别测试组最终得出的误识率相对较低,表明在实际应用过程中识别速度快、误差可控、识别范围较大,具有实际的应用价值。
At present,the weak coverage area of 5G wireless communication signal mostly adopts a single-layer recognition model,which can obtain the recognition results within a reasonable range,but lacks accuracy,leading to the improvement of the error recognition rate.Therefore,the analysis and research on the identification method of weak coverage area of 5G wireless communication signal based on random forest is proposed.Firstly,according to the requirements and standards of signal recognition,data acquisition and recognition environment preprocessing were carried out,directional recognition nodes were set.Secondly,random forest technology was integrated,a multi-level signal recognition structure was built,the accuracy of recognition results was improved.Finally,the construction of random forest communication signal recognition model was completed.Multi level signal integration processing was used to achieve area recognition.The test results show that the error rate of the random forest communication signal weak coverage area identification test group designed in this paper is relatively low,indicating that in the practical application process,the recognition speed is fast,the error is controllable,and the recognition range is large,which has practical application value.
作者
樊国庆
FAN Guoqing(China Tongfu Consulting Design&Research Institute Co.,Ltd.,Hanjiang Jiangsu 210019,China)
出处
《信息与电脑》
2022年第22期86-88,共3页
Information & Computer
关键词
随机森林
5G无线通信
信号覆盖
区域识别
random forest
5G wireless communication
signal coverage
area identification
