A wireless traffic forecasting method based on weighted federated learning
A wireless service and traffic forecasting technology, applied in forecasting, wireless communication, instruments, etc., can solve the problems of inaccurate capture of business traffic patterns of different base stations, inaccurate forecasting results, ignoring model differences, etc., to improve the overall forecasting accuracy, Avoid inaccurate predictions and avoid network congestion
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[0051] A wireless service traffic prediction method based on weighted federated learning, its system model is as follows figure 1 As shown, the wireless service flow model includes a control center and N base stations.
[0052] figure 1 The base station in represents that in the future communication network, the base station has three functions: network control according to intelligent algorithms, strong computing power, and wireless network access capability. "Intelligence" refers to the deployed machine learning model; "computing" refers to having strong CPU and GPU computing capabilities, and "access" refers to having wireless access capabilities. The combination of these three capabilities can realize the edge intelligence of the future network.
[0053] The core process of weighted federated learning training is as follows: figure 2 As shown, the model includes N base stations, where N=5, 10, 15, 20. Each base station contains 1448 time series points.
[0054] The s...
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