D2D communication network slice allocation method based on deep reinforcement learning

Abstract

The invention provides a D2D communication network slice allocation method based on deep reinforcement learning. The method comprises the following steps: S1, classifying communication services according to service types, and building a multi-service slice and D2D slice resource allocation model; s2, constructing a reinforcement learning model of slice resource allocation according to a Dueling DDQN algorithm; s3, defining a current state s, a next moment state s' and a current action a of a service slice for an agent in the Dueling DDQN algorithm, and constructing a reward r of a system by the states and the actions; and S4, learning of Dueling DDQN is carried out by using experience playback, and finally, an optimal solution of slice resource allocation is obtained. According to the method, resource allocation is carried out on the multi-service slices and the D2D slices, the multi-service slices and the D2D slices correspond to different uRLLC slices, mMTC slices, eMBB slices and D2D slices, a resource allocation model based on deep reinforcement learning is constructed in combination with a network slicing technology and a Duelling DDQN reinforcement learning algorithm, the slice resource allocation efficiency is improved, the communication requirements of various services are met, and the experience quality is optimal.

Description

technical field [0001] The present invention relates to the technical field of mobile Internet communication, and more specifically, to a method for allocating D2D communication network slices based on deep reinforcement learning. Background technique [0002] The popularity of the Internet has profoundly affected people's production, living and learning methods, and the Internet has become one of the important infrastructures supporting the development of modern society and technological progress. The advent of the 5G era has brought many excellent performances to wireless communications. 5G will greatly increase the transmission rate of communication, support massive device connections, and provide excellent performance with ultra-high reliability and ultra-low latency. 5G wireless networks support diverse business scenarios in different vertical industries, such as autonomous driving, smart home, augmented reality, etc. These business scenarios have different communicati...

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Problems solved by technology

This patent also has the problem of DQN's deep reinforcement learning. The estimated error will be transmitted and increased with the execution of the action, and eventually the agent cannot obtain the optimal strategy.

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