A low-latency and high-reliability v2v resource allocation method based on deep reinforcement learning

Abstract

A low-latency and high-reliability resource allocation method based on deep reinforcement learning proposed by the present invention considers NR‑V2X side chain resource allocation outside the coverage of the base station. The Q network schedules URLLC slice resources for V2V users in the 5G network. In order to maximize the energy efficiency of V2V communication and ensure the reliability and delay requirements of communication, a deep reinforcement learning architecture using centralized training and distributed execution is proposed, and a model that meets the above requirements is trained with the help of DDQN learning method. Transforming the modeling of goals and constraints in the resource allocation problem into the design of benefits in deep reinforcement learning can effectively solve the joint optimization problem of V2V user channel allocation and power selection, and can perform stably in the optimization of a series of continuous action spaces.

Description

technical field [0001] The invention relates to a vehicle networking technology, in particular to a vehicle networking resource allocation method, and more particularly, to a low-latency and high-reliability vehicle-to-vehicle (V2V) communication based on deep reinforcement learning resource allocation method. Background technique [0002] Vehicle-to-everything (V2X) is a typical application of Internet of Things (IoT) in the field of Intelligent Transportation System (ITS). The ubiquitous smart car network formed. The Internet of Vehicles shares and exchanges data according to agreed communication protocols and data exchange standards. It enables intelligent traffic management and services such as improved road safety, enhanced road condition awareness and reduced traffic congestion through real-time perception and collaboration between pedestrians, roadside facilities, vehicles, networks and the cloud. [0003] Deep reinforcement learning is a type of machine learning a...

AI Technical Summary

Problems solved by technology

All the above works do not take into account the energy consumption brought by V2V communication

At the same time, because the resource allocation scheme using the centralized reinforcement learning architecture needs to report the vehicle information to the central controller, the transmission overhead is large, and it increases sharply with the increase of the network size, which makes the method unable to be extended to large networks; In the resource allocation scheme using a fully decentralized reinforcement learning architecture, each agent can only observe part of the information related to itself, which makes the trained model inaccurate

Images