Abstract: The present application discloses a method and a device for user grouping and resource allocation in a NOMA-MEC system. The hybrid deep reinforcement learning algorithm proposed in the present application solves the hybrid problem of deep reinforcement learning that is difficult to deal with both discrete and continuous action spaces by using DDPG to optimize continuous actions and DQN to optimize discrete actions. Specifically, the algorithm determines a bandwidth allocation, an offloading decision, and a sub-channel allocation (user grouping) of the user device based on the user's channel state, in order to maximize the ratio of the computation rate to the consumed power of the system. The algorithm is well adapted to the dynamic characteristics of the environment and effectively improves the energy efficiency and spectrum resource utilization of the system.

Abstract: The present application discloses a method and a device for user grouping and resource allocation in a NOMA-MEC system. The hybrid deep reinforcement learning algorithm proposed in the present application solves the hybrid problem of deep reinforcement learning that is difficult to deal with both discrete and continuous action spaces by using DDPG to optimize continuous actions and DQN to optimize discrete actions. Specifically, the algorithm determines a bandwidth allocation, an offloading decision, and a sub-channel allocation (user grouping) of the user device based on the user's channel state, in order to maximize the ratio of the computation rate to the consumed power of the system. The algorithm is well adapted to the dynamic characteristics of the environment and effectively improves the energy efficiency and spectrum resource utilization of the system.

Abstract: The invention relates to a method for predicting benchmark value of unit equipment based on XGBoost algorithm and a system thereof, wherein the method comprises the following steps: the historical operation data of unit equipment is obtained, the data is preprocessed, and a data set containing a plurality of samples is constructed, and each sample includes the benchmark value of a plurality of parameters of the equipment corresponding to a plurality of features; RF out-of-bag estimation is used for feature importance calculation to eliminate the features with low importance; the data is standardized to eliminate the dimensional effects among features; the data set is input to construct an XGBoost model, and Bayesian super parameter optimization is conducted to obtain the prediction model of benchmark values; and the real-time data of equipment operation is input, and the benchmark values of various equipment parameters are predicted by the prediction model of benchmark values.