An Adaptive Rate Control Method Based on Mobility and DSRC/Wave Network Correlation Feedback

Abstract

The invention, which belongs to the technical field of car networking communinication, relates to an adaptive rate control method based on mobility and DSRC / WAVE network relevance feedback. The method comprises establishment of a traffic flow density prediction module, a t+1 time communication interference calculation module, an SINR calculation module, a t+1 time available link bandwidth calculation module, a channel congestion cost calculating module and an adaptive message generation rate calculation module. A traffic flow density value at a next time is predicted; according to the density value of the next time, a transmitting power, and a rate, an interference module of a communication process is established, a signal to noise ratio is calculated, and an available link bandwidth of a node at the next time is predicted; on the basis of mismatching of a transmission rate and mismatching of a transmission queue length, a channel congestion cost module is established, so that a message generation rate at the next time is adjusted adaptively. According to the method, adaptive rate adjustment is carried out in advance by using the prediction technology, so that the channel congestion is avoided; and the low communication delay and the high data packet transmission rate are guaranteed with the low calculation time and cost.

Description

technical field [0001] The invention relates to an adaptive rate control method for feedback related to mobility and DSRC / WAVE network, which belongs to the technical field of vehicle networking communication. Background technique [0002] The IntelliDrive Alliance predicts that in the future, with the deployment of the Internet of Vehicles, 80% of traffic accidents, 60% of traffic congestion, 15% of energy consumption, and 2-3 times increase in road network traffic capacity can be reduced. As the most challenging application in the Internet of Vehicles, collaborative vehicle safety applications have attracted more and more attention from scholars and researchers. In the collaborative vehicle safety application, each vehicle is equipped with WAVE wireless communication equipment, and the vehicles exchange information with neighbor nodes through the shared wireless channel through the DSRC protocol at a period of 100ms. These information include location, warning, and weather...

AI Technical Summary

Problems solved by technology

[0003] At present, there are many problems in the channel congestion control in the communication environment of the Internet of Vehicles: (1) The traditional research is to detect the channel occupancy rate, signal-to-noise ratio and the number of nodes at the current moment, and control when the performance index exceeds a certain threshold. A rough segmented control that cannot guarantee control accuracy and is not suitable for simultaneous triggering by multiple applications; (2) Traditional congestion control methods design control strategies to solve congestion after detecting channel congestion, which is passive Congestion control can only control congestion but cannot avoid congestion, and it takes a certain amount of time to recover from the congestion state, which has hysteresis; (3) Most researchers only consider interference within the communication range when establishing interference models , without considering the impact of interference within the detection range on communication. Due to the inherent characteristics of the Internet of Vehicles, vehicle nodes move rapidly on the road and the density changes frequently, which aggravates the impact of node interference on the wireless communication process. Therefore, in order to ensure safety For reliable communication of relevant information, a more accurate channel interference model must be established; (4) Some channel congestion control uses algorithms with high computational complexity such as particle swarm algorithm and genetic algorithm. Although better control effects can be obtained, the time complexity Too high to meet the real-time requirements required by the communication process

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