QoS based Deep Reinforcement Learning for V2X Resource Allocation

Bhadauria, Shubhangi; Shabbir, Zohaib; Roth-Mandutz, Elke; Georg, Fischer

doi:10.1109/blackseacom48709.2020.9234960

Cited by 9 publications

(6 citation statements)

References 4 publications

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“…They designed a reward function to ensure the latency constraints of the V2V links were satisfied. The authors of [ 12 ] proposed a QoS-aware resource allocation scheme based on the DRL framework in V2X communications, where they took QoS parameters such as the priority of V2V messages into consideration. The proposed scheme of [ 12 ] aims to maximize the sum rate of vehicle-to-infrastructure (V2I) links while satisfying the latency constraints of V2V links.…”

Section: Related Workmentioning

confidence: 99%

“…The authors of [ 12 ] proposed a QoS-aware resource allocation scheme based on the DRL framework in V2X communications, where they took QoS parameters such as the priority of V2V messages into consideration. The proposed scheme of [ 12 ] aims to maximize the sum rate of vehicle-to-infrastructure (V2I) links while satisfying the latency constraints of V2V links. The authors of [ 13 ] developed a power allocation problem in the cellular device-to-device (D2D)-based V2X communication network and mathematically solved the problem.…”

Section: Related Workmentioning

confidence: 99%

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Energy-Efficient Resource Allocation Based on Deep Q-Network in V2V Communications

Han

2023

Sensors

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Recently, with the development of autonomous driving technology, vehicle-to-everything (V2X) communication technology that provides a wireless connection between vehicles, pedestrians, and roadside base stations has gained significant attention. Vehicle-to-vehicle (V2V) communication should provide low-latency and highly reliable services through direct communication between vehicles, improving safety. In particular, as the number of vehicles increases, efficient radio resource management becomes more important. In this paper, we propose a deep reinforcement learning (DRL)-based decentralized resource allocation scheme in the V2X communication network in which the radio resources are shared between the V2V and vehicle-to-infrastructure (V2I) networks. Here, a deep Q-network (DQN) is utilized to find the resource blocks and transmit power of vehicles in the V2V network to maximize the sum rate of the V2I and V2V links while reducing the power consumption and latency of V2V links. The DQN also uses the channel state information, the signal-to-interference-plus-noise ratio (SINR) of V2I and V2V links, and the latency constraints of vehicles to find the optimal resource allocation scheme. The proposed DQN-based resource allocation scheme ensures energy-efficient transmissions that satisfy the latency constraints for V2V links while reducing the interference of the V2V network to the V2I network. We evaluate the performance of the proposed scheme in terms of the sum rate of the V2X network, the average power consumption of V2V links, and the average outage probability of V2V links using a case study in Manhattan with nine blocks of 3GPP TR 36.885. The simulation results show that the proposed scheme greatly reduces the transmit power of V2V links when compared to the conventional reinforcement learning-based resource allocation scheme without sacrificing the sum rate of the V2X network or the outage probability of V2V links.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Energy-Efficient Resource Allocation Based on Deep Q-Network in V2V Communications

Han

2023

Sensors

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“…Most of these papers present specific 3GPP scenarios using the latest Release-16 of the LTE based services aiming at highly reliable and real-time communications for automotive safety use cases. Physical layer structure, resource allocations [15], security, RSC methods [17] and QoS optimization triggered by dynamic radio conditions [16] are the main challenges for LTE V2X communications. However, none of these papers considers channel coding parameters (e.g., different decoding algorithms, number of iterations) in the optimization process for the realistic QoS indicators to be satisfied.…”

Section: Comparison Of the Proposed Approach With Published Literaturementioning

confidence: 99%

“…In [15] the authors present a QoS aware decentralized resource allocation for V2X communication based on a deep reinforcement learning (DRL) framework. The authors propose a scheme which incorporates the QoS parameter that reflects the latency required in both user equipment and the base station and the aim is to maximize the throughput of all vehicle-to-infrastructure (V2I) links, while meeting the latency constraints of vehicle-tovehicle (V2V) links.…”

Section: Introduction and Related Workmentioning

confidence: 99%

“…However, none of the above works includes channel coding optimizations at physical layer so that the required QoS levels are met. Specifically, some papers focus on RSC methods [17], whereas other papers focus on radio resource management [15] and the last subcategory of papers optimize the QoS papers triggered by dynamic radio conditions [16]. The idea of focusing on physical layer channel coding process and particularly using turbo coding in 5G V2X systems has been initially discussed in [20][21][22][23] without, however, the channel coding parameters being optimized for realistic QoS indicators.…”

Section: Introduction and Related Workmentioning

confidence: 99%

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3GPP 5G V2X Scenarios: Performance of QoS Parameters Using Turbo Codes

Kosmanos

Chaikalis

Savvas

2022

Telecom

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Cellular vehicle-to-everything (C-V2X) communication has recently gained attention in industry and academia. Different implementation scenarios have been derived by the 3rd Generation Partnership Project (3GPP) 5th Generation (5G) Vehicle-to-Everything (V2X) standard, Release 16. Quality of service (QoS) is important to achieve reliable communication and parameters which have to be considered are reliability, end-to-end latency, data rate, communication range, throughput and vehicle density for an urban area. However, it would be desirable to design a dynamic selecting system (with emphasis on channel coding parameters selection) so that all QoS parameters are satisfied. Having this idea in mind, in this work we examine nine V2X implementation scenarios using Long Term Evolution (LTE) turbo coding with a geometry-based efficient propagation model for vehicle-to-vehicle communication (GEMV), where we consider the above QoS parameters for SOVA, log-MAP and max-log-MAP decoding algorithms. Our study is suitable for 3GPP cooperative sensing, for the eight scenarios considering medium and large signal-noise-ratio (SNR) values. The proposed model is sustainable despite a doubled data rate, which results in a minimal bit error rate (BER) performance loss up to 1.85 dB. In this case tripling the data rate results in a further 1 dB loss. Moreover, a small loss up to 0.4 dB is seen for a vehicle speed increase from 60 km/h to 100 km/h. Finally, increasing vehicle density has no effect on the implemented 3GPP scenario considering end-to-end latency, irrespectively from the decoding algorithm.

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Intelligent Smart Transport System Using Internet of Vehicular Things—A Review

Vinodhini

Sujatha

2023

Lecture Notes in Electrical Engineering

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QoS based Deep Reinforcement Learning for V2X Resource Allocation

Cited by 9 publications

References 4 publications

Energy-Efficient Resource Allocation Based on Deep Q-Network in V2V Communications

Energy-Efficient Resource Allocation Based on Deep Q-Network in V2V Communications

3GPP 5G V2X Scenarios: Performance of QoS Parameters Using Turbo Codes

Intelligent Smart Transport System Using Internet of Vehicular Things—A Review

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