FLOW SUPPRESSION PREDICTION METHOD, ELECTRONIC DEVICE AND STORAGE MEDIUM

    公开(公告)号:US20240283728A1

    公开(公告)日:2024-08-22

    申请号:US18567597

    申请日:2022-05-23

    CPC classification number: H04L43/50 H04L41/145 H04L41/147

    Abstract: A method for predicting traffic suppression, an electronic device and a storage medium are disclosed. The method may include: determining a traffic value of suppression point according to a preset network traffic model which represents a mapping relationship between a numerical value of a network parameter of a transmission network and a traffic value, wherein the traffic value of suppression point is a traffic threshold of the transmission network under a current running policy; determining a suppression reference value of a target network parameter corresponding to the traffic value of suppression point; and acquiring a parameter prediction value corresponding to the target network parameter, and determining a traffic suppression prediction result according to the parameter prediction value and the suppression reference value.

    MODEL TRAINING METHOD, CHANNEL ADJUSTMENT METHOD, ELECTRONIC DEVICE, AND COMPUTER READABLE STORAGE MEDIUM

    公开(公告)号:US20240171359A1

    公开(公告)日:2024-05-23

    申请号:US18282817

    申请日:2022-03-14

    CPC classification number: H04L5/006 G06N20/00 H04L1/0004 H04L1/001 H04L1/0061

    Abstract: The present application provides a model training method, a channel adjustment method, an electronic device, and a computer readable storage medium, the model training method includes: collecting historical samples, with the historical samples including first scheduling information and first information corresponding to a historical data transmission, the first information representing a result of cyclic redundancy check, and the first scheduling information including first intermediate variable information in an adaptive modulation and coding process; and performing model training according to the historical samples to obtain a first prediction model, and during the model training, the first scheduling information is used as an input of the first prediction model, the first information is converted into second information corresponding to the historical data transmission to be used as an output of the first prediction model, and the second information represents a probability value of the result of the cyclic redundancy check.

Patent Agency Ranking