Deep Reinforcement Learning based Congestion Control for V2X Communication

被引：7

作者：

Roshdi, Moustafa ^{[1
]}

Bhadauria, Shubhangi ^{[1
]}

Hassan, Khaled ^{[2
]}

Fischer, Georg ^{[3
]}

机构：

[1] Fraunhofer IIS, Erlangen, Germany

[2] Robert Bosch GmbH, Gerlingen, Germany

[3] Friedrich Alexander Univ, Erlangen, Germany

来源：

2021 IEEE 32ND ANNUAL INTERNATIONAL SYMPOSIUM ON PERSONAL, INDOOR AND MOBILE RADIO COMMUNICATIONS (PIMRC) | 2021年

关键词：

C-V2X communication; Congestion control; DRL; FRAMEWORK;

D O I：

10.1109/PIMRC50174.2021.9569259

中图分类号：

TN [电子技术、通信技术];

学科分类号：

0809 ;

摘要：

In release 14 (Rel-14) Long Term Evolution (LTE), the 3rd generation partnership project (3GPP) standard has introduced Cellular Vehicle to Everything (C-V2X) communication to pave the way for future intelligent transport systems (ITS). C-V2X communication envisions supporting a diverse range of use cases with varying quality of service (QoS) requirements. For example, cooperative collision avoidance requires stringent reliability, while infotainment use cases require a high data throughput. C-V2X communication remains susceptible to performance degradation due to network congestion. This paper presents a centralized congestion control scheme for C-V2X communication based on the Deep Reinforcement Learning (DRL) framework. A performance evaluation of the algorithm is conducted based on system-level simulation based on TAPASCologne scenario in the Simulation of Urban Mobility (SUMO) platform. The results show the effectiveness of a DRL-based approach to achieve the packet reception ratio (PRR) as per the packet's associated QoS while maintaining the average measured Channel Busy Ratio (CBR) below 0.65.

引用

页数：6

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