Deep Reinforcement Learning based Congestion Control for V2X Communication

Roshdi, Moustafa; Bhadauria, Shubhangi; Hassan, Khaled; Georg, Fischer

doi:10.1109/pimrc50174.2021.9569259

Cited by 13 publications

(7 citation statements)

References 14 publications

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“…Roshidi et al. [7] employed RL to achieve lower channel utilization while increasing packet delivery ratio compared to packet dropping control. Choi et al.…”

Section: Related Workmentioning

confidence: 99%

“…Yang et al [6] employed a deep Q network (DQN)-based algorithm that responds to the target value of the Quality of Service (QoS) parameter in congestion control. Roshidi et al [7] employed RL to achieve lower channel utilization while increasing packet delivery ratio compared to packet dropping control. Choi et al [8] also employed RL, where each vehicle observes the CBR and selects the packet transmission rate that optimizes packet delivery rate (PDR) while maintaining higher channel utilization.…”

Section: Introductionmentioning

confidence: 99%

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Deep reinforcement learning‐based dual‐mode congestion control for cellular V2X environments

Yoon,

Lee,

Kim

2023

Electronics Letters

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The Society of Automotive Engineers (SAE) J2945/1 standard for Dedicated Short Range Communication (DSRC) environmentutilizes transmit (Tx) power control and rate control elements for the periodic BSM transmissions, which are intended to work in a complementary manner. An equivalent standard for the cellular vehicle‐to‐everything (C‐V2X) communication environment is J3161/1, but it eliminates Tx power control and uses only rate control. However, the consequence is the degraded update delay of neighbouring vehicles’ kinematics, potentially undermineing driving safety. In this Letter, the authors propose to retain the dual‐mode control in the C‐V2X environment and find a policy through reinforcement learning (RL) to adjust the rate control function to maintain synergy. Moreover, the authors can extract the RL‐created policy from the neural network so that it can be explicitly specified in the standard, and downloaded and used more conveniently by vehicles. Finally, the RL‐generated policy achieves a better packet delivery frequency than J2945/1 or J3161/1.

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“…Roshidi et al. [7] employed RL to achieve lower channel utilization while increasing packet delivery ratio compared to packet dropping control. Choi et al.…”

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep reinforcement learning‐based dual‐mode congestion control for cellular V2X environments

Yoon,

Lee,

Kim

2023

Electronics Letters

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“…However, using DQN may not be suitable for highly dynamic networks due to the instability learning of DQN models. In another work, Roshdi et al [35] present a Deep Deterministic Policy Gradient (DDPG)-based congestion control model. However, the enhancement from the work is merely to enhance DCC assessments.…”

Section: Related Workmentioning

confidence: 99%

Efficient Reinforcement Learning-Based Transmission Control for Mitigating Channel Congestion in 5G V2X Sidelink

Nguyen

Kuo

2022

IEEE Access

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Channel congestion has been an open challenge for vehicular networks due to the limited resource of communication channels. Explosion of channel access requests from a massive number of transmitter vehicles can exhaust bandwidth and then degrade transmission quality. The rapid drop of messages (because of the high bit error rate in the transmission congestion condition) can threaten the safety of connected vehicles. Maintaining congestion-free communications is then essential to improve the reliability for vehicular networks, including Cellular-V2X (C-V2X)-based cooperative intelligent transport systems and road-safety applications. In this work, we present a novel intelligent transmission control model, namely DEEPCUT, to automatically adjust the message broadcasting rate of a transmitter vehicle. DEEPCUT works based on a Double Deep Q-learning Networks with Prioritized Experience Relay framework. DEEPCUT encourages the transmitter vehicle to ( 1) reduce its broadcasting rate if the vehicle is maintaining a safe distance from its neighbors and (2) increase the rate if the vehicle is approaching the others at a high-risk distance, all done by using reward/punish strategies. The evaluation results show that DEEPCUT can cut up 16% redundant data while increasing 22% packet reception rate compared with baseline models, particularly in crowd vehicular communications. Our risk-based transmission control can be an excellent complement to address the congestion when the channel cannot satisfy every vehicle's resource requests. At best, the risk assessment-based approach in our congestion control method can provide a novel material to enhance Decentralized Congestion Control (DCC) for 5G V2X sidelink in the coming specifications.INDEX TERMS Vehicular network congestion, transmission control, reinforcement learning.

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“…The multiplicative effect traffic jams have in terms of CAM messages arises the need of adjusting the CAM message rate to reduce the usage of the downlink radio resources. The decentralized congestion control (DCC) algorithm, defined by ETSI in TS 102 687 [21], has been widely exploited within the framework of LTE V2V communications in [22] using synthetic data or in [23] using the TAPAS Cologne dataset. However, none of these studies focused on facing significant variations of road occupancy.…”

Section: A Alleviation Through V2x Application Layermentioning

confidence: 99%

On Alleviating Cell Overload in Vehicular Scenarios

Ortiz

Sallent

Camps‐Mur

et al. 2022

2022 IEEE 96th Vehicular Technology Conference (VTC2022-Fall)

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Fifth Generation (5G) networks will support countless new applications and new business models. One of the 5G paradigms is network slicing, which enables the integration of multiple logical networks each one tailored to the requirements of the different services that can be provided by both network operators and vertical industries. One of the services where 5G is expected to have a greatest impact is vehicular-to-everything (V2X) communications, which will have their stringent latency requirements now met. However, the mobility associated to vehicles can lead to cell overload compromising the required quality of service (QoS). To address this problem, in this paper we propose and evaluate the performance of three network overload alleviation techniques to control network congestion provoked by traffic jams using realistic vehicular traces in a network slicing environment. Firstly, we describe the architecture supporting V2X communications. Secondly, the network congestion control approaches are explained. Finally, after providing a complete description of the considered scenario, results will be detailed, showing that the network overload appearing during rush hour can be significantly reduced.

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Deep Reinforcement Learning based Congestion Control for V2X Communication

Cited by 13 publications

References 14 publications

Deep reinforcement learning‐based dual‐mode congestion control for cellular V2X environments

Deep reinforcement learning‐based dual‐mode congestion control for cellular V2X environments

Efficient Reinforcement Learning-Based Transmission Control for Mitigating Channel Congestion in 5G V2X Sidelink

On Alleviating Cell Overload in Vehicular Scenarios

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