5G Beyond 3GPP Release 15 for Connected Automated Mobility in Cross-Border Contexts

Vélez, Gorka; Martín, Ángel; Pastor, Giancarlo; Mutafungwa, Edward

doi:10.3390/s20226622

Cited by 21 publications

(11 citation statements)

References 25 publications

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“…Through the use of "cooperation awareness", the above four types of V2X applications can be used jointly to provide end users with smarter services. For example, vehicles, pedestrians, application servers, and road infrastructure can obtain local environmental information by receiving messages from sensors in nearby or other vehicles, enabling smarter services such as autonomous driving, vehicle warnings, and enhanced traffic management [20,21]. The four types of V2X communication modes are as follows:…”

Section: Vehicular Communication Modesmentioning

confidence: 99%

Deep reinforcement learning techniques for vehicular networks: Recent advances and future trends towards 6G

Mekrache

Bradai

Moulay

et al. 2022

Vehicular Communications

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Employing machine learning into 6G vehicular networks to support vehicular application services is being widely studied and a hot topic for the latest research works in the literature. This article provides a comprehensive review of research works that integrated reinforcement and deep reinforcement learning algorithms for vehicular networks management with an emphasis on vehicular telecommunications issues. Vehicular networks have become an important research area due to their specific features and applications such as standardization, efficient traffic management, road safety, and infotainment. In such networks, network entities need to make decisions to maximize network performance under uncertainty. To achieve this goal, Reinforcement Learning (RL) can effectively solve decision-making problems. However, the state and action spaces are massive and complex in large-scale wireless networks. Hence, RL may not be able to find the best strategy in a reasonable time. Therefore, Deep Reinforcement Learning (DRL) has been developed to combine RL with Deep Learning (DL) to overcome this issue. In this survey, we first present vehicular networks and give a brief overview of RL and DRL concepts. Then we review RL and especially DRL approaches to address emerging issues in 6G vehicular networks. We finally discuss and highlight some unresolved challenges for further study.

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Section: Vehicular Communication Modesmentioning

confidence: 99%

Deep reinforcement learning techniques for vehicular networks: Recent advances and future trends towards 6G

Mekrache

Bradai

Moulay

et al. 2022

Vehicular Communications

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“…Based on the system QoS being achieved, a decision is taken whether the transmitting autonomous vehicle communicates directly to the destination or through cooperative communication. In [13] the authors identify relevant future 5G 3GPP enhancements, specifically for releases beyond Release 15, and they outline how these releases will support highly automated driving in cross−border corridors. For this study a set of scenarios is investigated together with the communication requirements and the most relevant 5G features are proposed.…”

Section: Introduction and Related Workmentioning

confidence: 99%

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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“…Their evolution towards a connected swarm, cooperating to expand the individual understanding with sensors from surrounding vehicles and systems, becomes crucial to get a more holistic and broad view. This way, more complex applications such as platooning, cooperative driving or seethrough are possible [2].…”

Section: Introductionmentioning

confidence: 99%

Adaptive QoS of WebRTC for Vehicular Media Communications

Martín

Mejias

Fernandez

et al. 2022

2022 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

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Vehicles shipping sensors for onboard systems are gaining connectivity. This enables information sharing to realize a more comprehensive understanding of the environment. However, peer communication through public cellular networks brings multiple networking hurdles to address, needing in-network systems to relay communications and connect parties that cannot connect directly. Web Real-Time Communication (WebRTC) is a good candidate for media streaming across vehicles as it enables low latency communications, while bringing standard protocols to security handshake, discovering public IPs and transverse Network Address Translation (NAT) systems. However, the endto-end Quality of Service (QoS) adaptation in an infrastructure where transmission and reception are decoupled by a relay, needs a mechanism to adapt the video stream to the network capacity efficiently. To this end, this paper investigates a mechanism to apply changes on resolution, framerate and bitrate by exploiting the Real Time Transport Control Protocol (RTCP) metrics, such as bandwidth and round-trip time. The solution aims to ensure that the receiving onboard system gets relevant information in time. The impact on end-to-end throughput efficiency and reaction time when applying different approaches to QoS adaptation are analyzed in a real 5G testbed.

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5G Beyond 3GPP Release 15 for Connected Automated Mobility in Cross-Border Contexts

Cited by 21 publications

References 25 publications

Deep reinforcement learning techniques for vehicular networks: Recent advances and future trends towards 6G

Deep reinforcement learning techniques for vehicular networks: Recent advances and future trends towards 6G

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

Adaptive QoS of WebRTC for Vehicular Media Communications

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