A Resource Allocation Method for Heterogeneous Cloud Radio Access Networks Based on Deep Reinforcement Learning

Abstract

The invention relates to a heterogeneous cloud wireless access network resource allocation method based on deep reinforcement learning, which belongs to the technical field of mobile communication. In this method, it includes: 1) taking queue stability as a constraint, joint congestion control, user association, subcarrier allocation and power allocation, and establishing a stochastic optimization model to maximize the total throughput of the network; 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 dimensionality disaster problem; 3) Aiming at the complexity and dynamic variability of the wireless network environment, a transfer learning algorithm is introduced , using the small sample learning characteristics of transfer learning, the DRL algorithm can obtain the optimal resource allocation strategy even in the case of a small number of samples. The method can satisfy the requirement of service queue stability while maximizing the total throughput of the entire network. It has high application value in 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 ...

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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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