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Competition window size intelligent selection method based on chaotic Q-learning algorithm

A technology of competition window and learning algorithm, applied in the field of intelligent selection of competition window size based on chaotic Q-learning algorithm, can solve the problems of lack of dynamic learning/training, restricting coexisting network performance, unable to adjust system parameters, etc., to improve user experience , the effect of improving spectrum utilization and high throughput

Active Publication Date: 2019-07-19
CHONGQING UNIV OF POSTS & TELECOMM
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Problems solved by technology

Considering that the existing backoff mechanism (such as binary exponential backoff mechanism, etc.) lacks a dynamic learning / training process and cannot flexibly adjust system parameters according to the real-time network environment, this restricts the performance of the coexistence network to a certain extent.

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  • Competition window size intelligent selection method based on chaotic Q-learning algorithm
  • Competition window size intelligent selection method based on chaotic Q-learning algorithm
  • Competition window size intelligent selection method based on chaotic Q-learning algorithm

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Embodiment Construction

[0052] The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0053] Aiming at the coexistence problem of LTE and WiFi in the license-free frequency band (5 GHz) based on the LBT mechanism, the present invention proposes an intelligent selection method for the size of the competition window based on a chaotic Q-learning algorithm. Compared with the traditional back-off algorithm, the present invention can intelligently select the size of the LAA contention window based on the chaotic Q-learning algorithm. In other words, the LAA small base station can flexibly select a reasonable contention window size according to the real-time network environment. The process is as follows: figure 1 shown. First, in a certain state, the LAA small base station selects an action according to the ε-chaos greedy selection strategy and executes the action; then observes the environment and obtains the corresponding rewar...

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Abstract

The invention relates to a competition window size intelligent selection method based on a chaotic Q-learning algorithm, and belongs to the technical field of communication. According to the method, by constructing a chaotic Q-learning framework for LTE LAA and WiFi network scenarios, the LAA base station can intelligently select the optimal contention window size according to historical experience based on the current environment, thereby improving the performance of the coexistence network. According to the method, the throughput capacity of the coexistence system can be effectively improvedon the premise of ensuring the fairness of the coexistence system, and meanwhile, relatively low grouping delay can be obtained.

Description

[0001] Technical field: [0002] The invention belongs to the technical field of communication, and more specifically relates to a method for intelligently selecting the size of a competition window based on a chaotic Q-learning algorithm. [0003] Background technique: [0004] In the past ten years, the rapid development of mobile communication technology has gradually become an irreplaceable part of people's work and life, bringing great convenience to people. With the blowout growth of user business types and demands, new challenges will arise in the future wireless mobile communication systems in terms of technology, security and services. In order to enable users to obtain a better service experience, it is necessary to put forward higher requirements on indicators such as transmission rate, packet delay, and communication capacity of the communication system. At present, the industry mainly proposes two kinds of solutions to meet the rapidly growing demands of communica...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04W74/08
CPCH04W74/0808Y02D30/70
Inventor 裴二荣江军杰鹿逊易鑫刘珊朱冰冰朱磊李海星
Owner CHONGQING UNIV OF POSTS & TELECOMM