A realization method of mac protocol in vehicle network based on q-learning

Abstract

The invention discloses a method for realizing the MAC protocol of a vehicular network based on Q learning. In the method, the vehicle nodes use the Q learning algorithm to continuously learn interactively with the environment through repeated trial and error in the VANETs (vehicle ad hoc network) environment. According to the VANETs environment The given feedback signal (reward value) dynamically adjusts the competition window (CW), so that the node can always access the channel with the best CW (that is, the CW value selected when the reward value obtained from the surrounding environment is the largest), Ultimately, the goal of reducing data frame collision rate and transmission delay and improving the fairness of nodes accessing channels is achieved.

Description

technical field [0001] The invention relates to an implementation method of a vehicle-mounted network MAC protocol based on Q learning in a vehicle-mounted self-organizing network communication protocol, and belongs to the technical field of the Internet of Things. Background technique [0002] In recent years, with the rapid development of the transportation industry, the number of cars has increased dramatically. While the wide range of automobiles brings convenience to people's daily travel, various problems such as safety and traffic congestion have also occurred. In the 1980s, the University of California first proposed the concept of Intelligent Transportation System (ITS) to improve transportation efficiency, alleviate traffic congestion, and reduce traffic accidents. Today, with the rapid development of intelligent transportation systems and wireless communication technologies, the Internet of Vehicles has emerged as the times require. It is another symbol of future...

AI Technical Summary

Problems solved by technology

[0005] However, the above existing technologies are all improved on the basis of the BEB algorithm. Generally speaking, when the data collides and needs to be avoided, the CW value is multiplied. After the data is successfully sent, the CW will be restored to 15. If there are multiple nodes At the same time, the data is successfully sent, the CW value is restored to 15, and a collision occurs when the data is sent again.

The network load is less considered, and it is not suitable for networks with different load levels, that is, it is not scalable for traffic flows of different densities, and the fairness of channel access has not been effectively improved.

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