Support Vector Regression Method for Regional Economic Mid‐ and Long‐Term Predictions Based on Wireless Network Communication

Abstract

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.

Regional Economic Development in the AI Era: Methods, Opportunities, and Challenges

“…They can handle structured data (e.g., numerical and categorical data) and unstructured data (e.g., text, images, videos) alike, thus enabling the analysis of a wide range of economic indicators and factors. For example, Support Vector Machines and Random Forests, two popular ML algorithms, have been used for regression and classification tasks in economic data analysis [26,29]. DL models, such as CNN and RNN, can deal with high-dimensional and sequential data, making them particularly useful for time-series economic data analysis [17,20].…”

“…ML models, such as decision trees, support vector machines, and ensemble methods, have been extensively used in predictive economic modelling. For instance, Dong [26] employed Support Vector Regression for regional economic mid-and long-term predictions. Ensemble learning techniques, such as Random Forests and Gradient Boosting, have been used to predict economic outcomes with greater accuracy by combining multiple base learners.…”

“…The remaining papers cover a diverse range of applications, from economic predictions [26] to the use of data mining techniques for regional economic analysis [27]. They all contribute to the understanding of the transformative potential and challenges of AI in analyzing regional economic development.…”