Data packet transmission intelligent decision-making method based on deep reinforcement learning

Abstract

The invention relates to a data packet transmission intelligent decision-making method based on deep reinforcement learning. The method comprises the following steps: constructing a deep neural network model; designing and initializing a state space and a behavior space; acquiring current state information and historical state information of data transmission, and inputting the current state information and the historical state information into a state space; storing historical state information by adopting an experience playback mechanism; iteratively executing the step (3) and the step (4) for T times, and ending the round; updating the target value neural network parameter [theta]', and endowing the target value neural network with the latest parameter theta of the original value neuralnetwork; and (3) iteratively executing the steps (2) to (5) until the number of iterations reaches a preset round upper limit N or the deep neural network is converged, and terminating and automatically obtaining a data transmission strategy which meets the multi-constraint condition limitation and is lower in energy consumption. According to the invention, the user service quality is improved, the data transmission energy consumption is reduced, and the intelligent decision-making capability of data transmission of a communication network in a highly complex dynamic environment is effectively improved.

Description

technical field [0001] The invention relates to the technical field of wireless communication, in particular to an intelligent decision-making method for data packet transmission based on deep reinforcement learning. Background technique [0002] With the development of the Internet of Things and the large-scale popularization of mobile terminals, wireless traffic has surged, and energy consumption caused by data transmission has also increased significantly. In addition, with the introduction of new concepts such as green networks and smart networks, reducing energy consumption has become increasingly important. How to reasonably formulate data transmission strategies to achieve stable data transmission and reduce transmission energy consumption while ensuring user service experience has become a topic of great concern. [0003] Data transmission in the current context is becoming complex, diverse and dynamic, and data transmission decisions are faced with the test of a hu...

AI Technical Summary

Problems solved by technology

At the same time, considering the uncertainty of the data packet size and the number of requests, as well as the strong demand of users for transmission delay, traditional algorithms cannot comprehensively consider many constraints and reduce transmission energy consumption when dealing with such complex and dynamic data transmission requirements. , it is difficult to obtain the ideal optimization effect

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