Heterogeneous cloud wireless access network resource allocation method based on deep reinforcement learning

Abstract

The invention relates to a heterogeneous cloud wireless access network resource allocation method based on deep reinforcement learning, and belongs to the technical field of mobile communication. Themethod comprises the following steps: 1) taking queue stability as a constraint, combining congestion control, user association, subcarrier allocation and power allocation, and establishing a random optimization model for maximizing the total throughput of the network; 2) considering the complexity of the scheduling problem, the state space and the action space of the system are high-dimensional,and the DRL algorithm uses a neural network as a nonlinear approximation function to efficiently solve the problem of dimensionality disasters; and 3) aiming at the complexity and the dynamic variability of the wireless network environment, introducing a transfer learning algorithm, and utilizing the small sample learning characteristics of transfer learning to enable the DRL algorithm to obtain an optimal resource allocation strategy under the condition of a small number of samples. According to the method, the total throughput of the whole network can be maximized, and meanwhile, the requirement of service queue stability is met. And the method has a very high application value in a mobile communication system.

Description

technical field [0001] The invention belongs to the technical field of mobile communication, and relates to a resource allocation method of a heterogeneous cloud wireless access network based on deep reinforcement learning. Background technique [0002] With the rapid development of communication technology, human beings have entered the era of ubiquitous mobile Internet and intercommunication. A series of information technologies such as smart terminals, wireless local area networks (WLAN, WIFI), Internet of Vehicles, and mobile payment have brought more high-quality and convenient experiences to people's lives. Wireless communication technologies have developed into a network with different bandwidths, modulation methods, and coverage heterogeneous cloud wireless access network. Due to the traditional static network working mode, a series of problems such as information independence among various networks, resource sharing and low spectrum utilization rate will seriously ...

AI Technical Summary

Problems solved by technology

[0007] A method for resource allocation of heterogeneous cloud radio access networks based on deep reinforcement learning, in which method includes: 1) taking queue stability as a constraint, joint congestion control, user association, subcarrier allocation and power allocation, and establishing a network A stochastic optimization model for maximizing the total throughput; 2) Considering the complexity of the scheduling problem, the state space and action space of the system are high-dimensional, and the DRL algorithm uses the neural network as a nonlinear approximation function to efficiently solve the curse of dimensionality problem; 3) In view of the complexity and dynamic variability of the wireless network environment, the transfer learning algorithm is introduced, and the small sample learning characteristics of transfer learning are used to make the DRL algorithm obtain the optimal resource allocation strategy even in the case of a small number of samples.

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