Citation: Chen, M.; Cui, H.; Nie, M.;
Chen, Q.; Yang, S.; Du, Y.; Dai, F.
Cooperative Transmission
Mechanism Based on Revenue
Learning for Vehicular Networks.
Appl. Sci. 2022, 12, 12651.
https://doi.org/10.3390/
app122412651
Academic Editors: Panagiotis
Sarigiannidis, Thomas Lagkas,
Alexandros-Apostolos Boulogeorgos,
Vasileios Argyriou and Pantelis
Angelidis
Received: 1 November 2022
Accepted: 6 December 2022
Published: 9 December 2022
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
published maps and institutional affil-
iations.
Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Article
Cooperative Transmission Mechanism Based on Revenue
Learning for Vehicular Networks
Mingyang Chen
1,2
, Haixia Cui
1,2,
* , Mingsheng Nie
1,2
, Qiuxian Chen
1,2
, Shunan Yang
1,2
, Yongliang Du
3
and Feipeng Dai
1,2
1
School of Electronics and Information Engineering, South China Normal University, Foshan 528225, China
2
School of Physics and Telecommunication Engineering, South China Normal University,
Guangzhou 510006, China
3
China Shift Internet Co., Ltd., Guangzhou 510000, China
* Correspondence: cuihaixia@scnu.edu.cn
Abstract:
With the rapid development of science and technology and the improvement of people’s
living standards, vehicles have gradually become the main means of travel. The increase in vehicles
has also brought about an increasing incidence of car accidents. In order to reduce traffic accidents,
many researchers have proposed the use of vehicular networks to quickly transmit information. As
long as these vehicles can receive information from other vehicles or buildings nearby in a timely
manner, they can avoid accidents. In vehicular networks, the traditional double connection technique,
through interference coordination scheduling strategy based on graph theory, can ensure the fairness
of vehicles and obtain suitable neighborhood interference resistance with limited computing resources.
However, when a base station transmits data to the vehicular user, the nearby base station and
the vehicular network user may be in a state of suspended communication. Thus, the resource
utilization of the above double connection vehicular network is not sufficient, resulting in a waste of
resources. To solve this issue, this paper presents a study based on earnings learning with a vehicular
network multi-point collaborative transmission mechanism, in which the vehicular network users
communicate with the surrounding collaborative transmission. We use the Q-learning algorithm in
the reinforcement learning process to enable vehicular network users to learn from each other and
make cooperative decisions in different environments. In reinforcement learning, the agent makes
a decision and changes the state of the environment. Then, the environment feeds back the benefit
to the agent through the related algorithm so that the agent gradually learns the optimal decision.
Simulation results demonstrate the superiority of our proposed approach with the revenue machine
learning model compared with the benchmark schemes.
Keywords:
vehicular networks; cooperative transmission; reinforcement learning; interference
coordination; driverless technology
1. Introduction
In recent years, with the increasing improvement of living standards, people’s demand
for transportation tools has gradually increased, and the number of civil vehicles has also
been increasing. By the end of 2020, the number of civil cars in China reached 280 million,
and the mass popularization of cars has caused increasingly serious urban traffic jams. The
ensuing traffic safety issues have attracted people’s attention. Even though the government
has come up with new laws and regulations to regulate drivers’ driving behavior, there are
nearly 200,000 traffic accidents in China every year, resulting in a large number of casualties
and property losses. The development of national road traffic should not only pay attention
to the quantity and ignore the quality. While realizing the improvement of people’s living
standard, we should also deal with the social hidden dangers brought by it. Given driver
problems, manual driving can reduce some dangers for traffic safety. Therefore, in order to
Appl. Sci. 2022, 12, 12651. https://doi.org/10.3390/app122412651 https://www.mdpi.com/journal/applsci
