移动通信目标信号实时识别仿真研究

Real-Time Recognition of Mobile Communication Target Signal and Its Simulation

移动通信目标信号的准确识别,可加强网络的管理与监控。移动通信目标信号的准确识别的重点是识别率较高的强识别器,而传统的贝叶斯算法中不能通过计算移动通信目标信号样本的权重和分布,将弱识别器通过迭代算法组合成强识别器,导致移动通信目标信号的识别精度下降。提出一种基于机器学习的移动通信目标信号实时识别方法。依据ID3算法将移动通信目标信号中具有最高增益的数据属性定义为当前移动通信网络数据流节点的识别测试数据属性,选取具有代表性的属性特征形成子集进行机器学习,并利用SVN算法将移动通信网络中目标信号的识别问题转换为求解目标信号特征高维空间的最优"超平面"问题,并通过迭代运算用几个弱识别器组建出一个实时信号识别率较高的强识别器,有效的完成了移动通信目标信号实时识别。仿真结果表明,基于机器学习的移动通信目标信号实时识别方法为网络稳定运行提供了有力的支持。

A real-time recognition method for target signal of mobile communication based on machine learning is proposed.Based on the ID3 algorithm,the data attribute with the highest gain in target signal of mobile communication is defined as the recognition test data attribute of the data flow node in current mobile communication network.The representative attributes are selected to form the subset and make machine learning.The SVN algorithm is used to convert the recognition problem of target signal in mobile communication network into the problem for solving the optimal hyperplane of target signal characteristics in high dimensional space.And by iterative operation,several weak recognizers are used to form a strong recognizer with high real-time signal recognition rate,so as to effectively complete the target signal recognition of mobile communication in real-time.The simulation results show that the realtime recognition method for target signal of mobile communication based on machine learning provides strong support for the stable operation of the network.