User Traffic prediction System in Wireless Networks

Abstract

The invention discloses a user traffic prediction system in a wireless network, which comprises four subsystems, namely, basic feature extraction, user movement trajectory prediction, user traffic preference analysis and regional traffic prediction subsystems. Firstly, the user traffic is analyzed to extract user features initially. According to the user's moving trajectory data in the region, thenext step direction of the user is predicted based on the convolution neural network model. According to the traffic situation of users in the region, the next period traffic of users is predicted based on the circulating neural network model. Combined with user movement trajectory prediction and traffic prediction, the traffic distribution of the whole region in the next period is predicted. Theinvention predicts the network traffic of each wireless access point in the next period of the area by analyzing the wireless network connection and the traffic usage of the wireless network users inthe area. It can be used to solve the problems of bandwidth and channel assignment of multiple routers in wireless network. The problem of traffic usage prediction for specific users is solved. It can also be used to predict the trajectory of the user's movement.

Description

technical field [0001] The invention belongs to the technical field of data mining, and in particular relates to a user traffic prediction system in a wireless network. Background technique [0002] With the development of mobile communication technology and the rapid popularization of smart mobile devices (such as smart phones, wireless sensors), users use mobile devices more and more frequently, generating a large amount of wireless network traffic. Due to the high randomness of the user's activity range, network congestion will inevitably occur in a certain period of time and in a certain area, while other areas have smooth communication and unbalanced communication load, which will affect user experience, cause user loss, and even interfere with The normal work of smart mobile devices (such as the network module of unmanned vehicles) will cause potential safety hazards. Therefore, the problem of predicting network traffic distribution within a region has attracted the a...

AI Technical Summary

Problems solved by technology

However, this method is too rough. It only makes continuous predictions based on the traffic size in the previous period, and cannot cope with sudden changes in traffic, nor can it discover hidden user habits and periodic events in traffic that significantly affect traffic changes.

In fact, regional traffic is a combination of the traffic of each user. If the characteristics of a single user cannot be considered, it is difficult to capture the details of traffic changes. For example, when a user moves from A to B at a certain moment, the traffic of A will inevitably decrease. The flow rate at B may increase correspondingly, and the prediction method that only considers flow records cannot explain this reasonable change

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