In recent years, wireless sensor network technology has continued to develop, and it has become one of the research hotspots in the information field. People have higher and higher requirements for the communication rate and network coverage of the communication network, which also makes the problems of limited wireless mobile communication network coverage and insufficient wireless resource utilization efficiency become increasingly prominent. This article is aimed at studying a support vector regression method for long-term prediction in the context of wireless network communication and applying the method to regional economy. This article uses the contrast experiment method and the space occupancy rate algorithm, combined with the vector regression algorithm of machine learning. Research on the laws of machine learning under the premise of less sample data solves the problem of the lack of a unified framework that can be referred to in machine learning with limited samples. The experimental results show that the distance between AP1 and AP2 is 0.4 m, and the distance between AP2 and Client2 is 0.6 m. When BPSK is used for OFDM modulation, 2500 MHz is used as the USRP center frequency, and 0.5 MHz is used as the USRP bandwidth; AP1 can send data packets. The length is 100 bytes, the number of sent data packets is 100, the gain of Client2 is 0-38, the receiving gain of AP2 is 0, and the receiving gain of AP1 is 19. The support vector regression method based on wireless network communication for regional economic mid- and long-term predictions was completed well.

中文翻译：

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基于无线网络通信的区域经济中长期预测支持向量回归方法

近年来，无线传感器网络技术不断发展，已成为信息领域的研究热点之一。人们对通信网络的通信速率和网络覆盖范围的要求越来越高，这也使得无线移动通信网络覆盖范围有限、无线资源利用效率不足的问题日益突出。本文旨在研究无线网络通信背景下长期预测的支持向量回归方法，并将该方法应用于区域经济。本文采用对比实验法和空间占用率算法，结合机器学习的向量回归算法。在样本数据较少的前提下研究机器学习规律，解决了在样本有限的机器学习中缺乏可以参考的统一框架的问题。实验结果表明，AP1和AP2之间的距离为0.4m，AP2和Client2之间的距离为0.6m。使用BPSK进行OFDM调制时，USRP中心频率使用2500MHz，USRP带宽使用0.5MHz；AP1 可以发送数据包。长度为100字节，发送数据包数为100，Client2的增益为0-38，AP2的接收增益为0，AP1的接收增益为19。 基于无线网络的支持向量回归方法做好区域经济中长期预测沟通工作。