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.

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 cycli

Abstract: An outer loop initial value adjustment method, a device, and a readable storage medium are disclosed. The method may include: acquiring feature information of a plurality of User Equipment (UEs) scheduled by a cell; performing raster division on the feature information of the plurality of UEs to obtain a plurality of rasters; determining an outer loop corresponding to each of the rasters, where an outer loop value of each raster is within a target Block Error Rate (BLER) range; determining a target raster corresponding to a target UE according to the feature information of the target UE, where the target UE is one of the plurality of UEs scheduled by the cell; and determining the outer loop value corresponding to the target raster as an initial outer loop value of the target UE.

Abstract: A method and apparatus for determining an outer loop value, a device, and a storage medium are disclosed. The method may include: determining a pre-trained outer loop initialization model based on current feature data of a user equipment (S11); and determining an initialized outer loop value of the user equipment based on a current air interface measurement value of the user equipment and the outer loop initialization model (S12).