A base station caching algorithm for user behavior prediction based on a deep learning neural network

Abstract

The invention discloses a base station caching algorithm for user behavior prediction based on a deep learning neural network, and the algorithm comprises the following steps: recording a base stationservice user demand for a period of time, arranging the demand, carrying out the preprocessing, and determining a training set U of the neural network; utilizing a neural network algorithm in deep learning, including DNN and RNN, to carry out training fitting on the training set data U, and establishing a user demand behavior model, and predicting the demand of the user at the next time by usingthe user demand behavior model, and pre-caching data obtained by model prediction in a cache of the base station by using a base station caching strategy so as to ensure that the data can be directlyobtained from the cache of the base station when a user needs the data. Real demands of the user are recorded and compared with prediction data, the model is perfected continuously, and the steps 3-5are continued; the demand behavior model of the user is continuously perfected, and the prediction precision is improved.

Description

technical field [0001] The invention belongs to the field of wireless communication, and relates to a communication technology of a base station caching strategy, in particular to a base station caching algorithm for user behavior prediction based on a deep learning neural network. Background technique [0002] With the commercialization and widespread deployment of 4G networks, users can enjoy increasingly high-speed data services. This further stimulates the use of mobile devices (smart machines, tablet PCs, etc.) by users, so the traffic load of the cellular network increases sharply. A recent forecast shows that data traffic on wireless networks will reach 190 exabytes in 2018, and more importantly, more than half of this will be transmitted over 4G / LTE networks. The traditional cellular structure wireless access network is widely used in 4G. Although some advanced technologies, such as relay, OFDM, MIMO, and anti-interference measures, are used to increase system capac...

AI Technical Summary

Problems solved by technology

The traditional cellular structure wireless access network is widely used in 4G. Although some advanced technologies, such as relay, OFDM, MIMO, and anti-interference measures, are used to increase system capacity and improve service quality, it still cannot meet the needs of continuous growth. The single traditional cellular structure radio access network is increasingly becoming a bottleneck that seriously affects user experience

[0003] Although wider network coverage is achieved through low cost and low power consumption, due to the relatively weak load capacity of small cells compared with traditional cells, how to maintain the carrying capacity of small cell base stations during peak hours has become a technical bottleneck

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