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: A method for recognizing a fog concentration of a hazy image includes inputting a target hazy image into a pre-trained directed acyclic graph (DAG) support vector machine to acquire a fog concentration of the target hazy image. The fog concentration of the target hazy image is represented based on a prebuilt multi-feature model, and the feature vector in the multi-feature model includes at least one of a color feature, a dark channel feature, an information quantity feature and a contrast feature.

Abstract: Disclosed are a network traffic classification method and system based on an improved K-means algorithm. The method comprises: judging whether a total number NIC of network traffic data points in an initial clustering center set reaches an expected number k of network traffic clusters, if the k is not reached, calculating candidate metric values of network traffic data points in a high-density network traffic data point set, selecting a network traffic data point having the maximum candidate metric value, adding same into an initial clustering center set, removing the network traffic data point from the high-density network traffic data point set, then repeating the step until the total number NIC of network traffic data points in the initial clustering center set reaches the k, and ending the step. The method and system can ensure high network traffic classification accuracy.

Abstract: Disclosed are a network traffic classification method and system based on an improved K-means algorithm. The method comprises: judging whether a total number NIC of network traffic data points in an initial clustering center set reaches an expected number k of network traffic clusters, if the k is not reached, calculating candidate metric values of network traffic data points in a high-density network traffic data point set, selecting a network traffic data point having the maximum candidate metric value, adding same into an initial clustering center set, removing the network traffic data point from the high-density network traffic data point set, then repeating the step until the total number NIC of network traffic data points in the initial clustering center set reaches the k, and ending the step. The method and system can ensure high network traffic classification accuracy.

Abstract: A method for recognizing a fog concentration of a hazy image includes inputting a target hazy image into a pre-trained directed acyclic graph (DAG) support vector machine to acquire a fog concentration of the target hazy image. The fog concentration of the target hazy image is represented based on a prebuilt multi-feature model, and the feature vector in the multi-feature model includes at least one of a color feature, a dark channel feature, an information quantity feature and a contrast feature.