5G Internet of Vehicles V2V resource allocation method adopting depth deterministic strategy gradient algorithm

Abstract

The invention provides a vehicle-to-vehicle (V2V) communication resource allocation method based on a depth deterministic policy gradient (DDPG) algorithm, wherein the V2V communication accesses a 5G network by using a network slicing technology, an optimal V2V user channel allocation and transmitting power joint optimization strategy is obtained by using a deep reinforcement learning optimization strategy, V2V users select proper transmitting power and channels so as to reduce the mutual interference between the V2V links; the total system throughput of the V2V links is maximized under the condition that the link delay constraint is met. According to the method, the problem of joint optimization of V2V user channel allocation and power selection can be effectively solved by using the DDPG algorithm, and the performance is stable in optimization of a series of continuous action spaces.

Description

technical field [0001] The present invention relates to a vehicle networking technology, in particular to a resource allocation method for the vehicle network, and more specifically, to a 5G vehicle network using a Deep Deterministic Policy Gradient (DDPG) algorithm. Vehicle (Vehicle-to-Vehicle, V2V) communication resource allocation method. Background technique [0002] Vehicle-to-everything (V2X) is a typical application of the Internet of Things (IoT) in the field of Intelligent Transportation System (ITS). A ubiquitous smart car network formed. The Internet of Vehicles shares and exchanges data according to agreed communication protocols and data interaction 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 among pedestrians, roadside facilities, vehicles, networks, and the cloud. [0003] Proper resource allo...

AI Technical Summary

Problems solved by technology

However, the existing V2V resource allocation algorithm based on deep reinforcement learning cannot meet the differentiated service requirements of high-bandwidth, large-capacity, ultra-reliable and low-latency scenarios under 5G networks.

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