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Terminal access selection method based on deep reinforcement learning

A technology of reinforcement learning and terminal access, which is applied in the field of communication networks to achieve the effects of improving resource utilization, transmission rate, and transmission stability.

Inactive Publication Date: 2020-02-18
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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  • Abstract
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AI Technical Summary

Problems solved by technology

[0005] In order to solve the signal access switching problem caused by the movement of terminals in heterogeneous networks, the present invention proposes a terminal access selection method based on deep reinforcement learning, and implements heterogeneous network access selection based on terminal self-learning

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  • Terminal access selection method based on deep reinforcement learning
  • Terminal access selection method based on deep reinforcement learning
  • Terminal access selection method based on deep reinforcement learning

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Embodiment Construction

[0037] In order to facilitate those of ordinary skill in the art to understand the present invention, at first the following technical terms are defined:

[0038] 1. Q-Learning

[0039] A reinforcement learning algorithm, the agent perceives the environment by performing actions in the environment to obtain a certain reward, so as to learn the mapping strategy from state to action to maximize the reward value.

[0040] 2. Deep-Q-Learning (DQN)

[0041]DQN is the first to combine deep learning models with reinforcement learning to successfully learn control policies directly from high-dimensional inputs. By introducing the method of expected delayed return, the MDP (Markov Decision Process) problem under the condition of lack of information is solved. It can be considered that DQN learning is based on the instantaneous strategy and is a special deep reinforcement learning method of an independent model.

[0042] 3. Adaptive

[0043] According to the data characteristics of t...

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Abstract

The invention discloses a terminal access selection method based on deep reinforcement learning, is applied to the field of communications, and the problem of signal access switching caused by movement of a terminal in a heterogeneous network is solved. According to the invention, various factors such as service quality requirements of different types of businesses and service quality assurance capabilities of different types of access nodes are comprehensively considered; and perception decision making on the current network environment is carried out by adopting a Dep-Q-Learning deep reinforcement learning algorithm, thereby realizing terminal intelligent access selection decision making based on environment and resource perception. The communication experience of the user is effectivelyimproved, and the algorithm has self-adaptability and online learning capability.

Description

technical field [0001] The invention belongs to the field of communication networks, and in particular relates to a terminal switching access technology in a wireless heterogeneous network. Background technique [0002] With the explosive growth of the number of mobile terminals and traffic, a single network can no longer meet performance requirements such as high coverage, low latency, and high bandwidth. Therefore, the new-generation network will integrate existing heterogeneous networks to maximize network performance and efficiency. On the basis of this network, the terminal access strategy is a problem that is expected to be solved. [0003] Due to the diversity of terminal services, terminals have different requirements for signal strength, delay, and signal-to-noise ratio. The terminals that the network provides services include not only smartphones, but also IoT devices such as smart homes and vehicle-mounted smart terminals. The needs of these terminals include vid...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04W48/08H04W48/16
CPCH04W48/08H04W48/16
Inventor 黄晓燕成泽坤杨宁冷甦鹏
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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