Machine Learning for 6G Wireless Networks: Carrying Forward Enhanced Bandwidth, Massive Access, and Ultrareliable/Low-Latency Service

Du, Jun; Jiang, Chunxiao; Wang, Jian; Ren, Yong; Debbah, Mérouane

doi:10.1109/mvt.2020.3019650

Cited by 260 publications

(91 citation statements)

References 42 publications

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“…If it does, it will be excluded from the candidate vehicle J. Lastly, cooperative vehicle selection is carried out according to Eq. (12). In fact, the selection is based on the quality of communication and calculation.…”

Section: B Cooperative Vehicle Selection Methodsmentioning

confidence: 99%

“…When the vehicles and surrounding vehicles belong to a same company (i.e., taxi), or the surrounding vehicles are the public transportation system, the RSUs can obtain the history moving trajectory of the surrounding vehicles. With the development of machine learning (ML) methods, we can use the appropriate ML methods to predict vehicle trajectories [11], [12], and select a reliable cooperative vehicle node based on a given criteria.…”

Section: Introductionmentioning

confidence: 99%

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Task Offloading and Serving Handover of Vehicular Edge Computing Networks Based on Trajectory Prediction

Yang

Chen

et al. 2021

IEEE Access

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Vehicular edge computing (VEC) has emerged as a promising paradigm to ensure the realtime task processing caused by the emerging 5G or high level intelligent assisted driving applications. The computing tasks can be processed via the edge services deployed at the roadside units (RSUs) or moving vehicles. However, the high dynamic topology of the vehicular communication system and the time-varying available computing resources in RSUs make a challenge of the efficient task offloading of vehicles. In this paper, we consider an efficient task offloading scheme for VEC networks based on trajectory prediction, we focus on the serving handover between the adjacent RSUs. The moving vehicles can cooperate with RSUs or the surrounding vehicles for task processing. To reduce the latency of task transmission between vehicles, we present a cooperative vehicle selection method based on trajectory prediction. Then, we propose an efficient task offloading scheme based on deep reinforcement learning (DRL), while the dynamically available computing and communication resources are considered jointly. The simulation results show that the proposed task offloading scheme has great advantages in improving the utility of vehicles.INDEX TERMS Internet of vehicles, edge computing, trajectory prediction, task offloading, deep reinforcement learning

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Section: B Cooperative Vehicle Selection Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Task Offloading and Serving Handover of Vehicular Edge Computing Networks Based on Trajectory Prediction

Yang

Chen

et al. 2021

IEEE Access

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“…In [40], the authors outline the concept of trustworthy autonomy for 6G, clarify how explainable AI can generate the qualitative and quantitative modalities of trust, and provide associated key performance indicators (KPIs) for measuring trust. In [41], Du et al summarized some intelligent approaches of applying AI and ML tools to optimize 6G networks, including THz communications, energy management, security, mobility management, and resource allocation. To facilitate a clearer illustration, the aforementioned works with major contributions and categorized topics are listed chronologically in Table I.…”

Section: Feb Vehicularmentioning

confidence: 99%

The Road Towards 6G: A Comprehensive Survey

Jiang

Han

Habibi

et al. 2021

IEEE Open J. Commun. Soc.

919

241

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As of today, the fifth generation (5G) mobile communication system has been rolled out in many countries and the number of 5G subscribers already reaches a very large scale. It is time for academia and industry to shift their attention towards the next generation. At this crossroad, an overview of the current state of the art and a vision of future communications are definitely of interest. This article thus aims to provide a comprehensive survey to draw a picture of the sixth generation (6G) system in terms of drivers, use cases, usage scenarios, requirements, key performance indicators (KPIs), architecture, and enabling technologies. First, we attempt to answer the question of "Is there any need for 6G?" by shedding light on its key driving factors, in which we predict the explosive growth of mobile traffic until 2030, and envision potential use cases and usage scenarios. Second, the technical requirements of 6G are discussed and compared with those of 5G with respect to a set of KPIs in a quantitative manner. Third, the state-of-the-art 6G research efforts and activities from representative institutions and countries are summarized, and a tentative roadmap of definition, specification, standardization, and regulation is projected. Then, we identify a dozen of potential technologies and introduce their principles, advantages, challenges, and open research issues. Finally, the conclusions are drawn to paint a picture of "What 6G may look like?". This survey is intended to serve as an enlightening guideline to spur interests and further investigations for subsequent research and development of 6G communications systems.

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“…This means that AI will not merely be an application but an inherent part of the infrastructure, and of the network management and operations [66]. The usage of AI for physical, network, and application layers was described in [67]. While for network and application layers the ideas come from existing research in ML and Self-Organising Networks (SON)/autonomic networking, the additional novel aspect is the full application of AI within the physical layer.…”

Section: Targeted Architectural Characteristicsmentioning

confidence: 99%

Why do we need 6G?

Bassoli¹,

Fitzek²,

Strinati³

2021

ITU-J FET

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The study and design of 5G seems to have reached its end and 5G communication systems are currently under deployment. In parallel, 5G standardization is as Release 16, which is going to complete the definition and the design guidelines of the 5G radio access network. Because of that, the interest of the scientific and industrial communities has already started focusing on the future 6G communication networks. The preliminary definition of future technology trends towards 2030, given by major standardization bodies, and the flagship 6G projects worldwide have started proposing various visions about what 6G will be. Side by side, various scientific articles, addressing the initial characterisation of 6G, have also been published. However, considering the promises of 5G, can 6G represent a significant technological advancement to justify a so-called new generation? In fact, now, 5G softwarized networks may just imply continuous network software upgrades (as it happens for the Internet) instead of new generations every ten years. This article starts describing the main characteristics that made 5G a breakthrough in telecommunications, also briefly introducing the network virtualisation and computing paradigms that have reformed telecommunications. Next, by providing rigorous definition of the terminology and a survey of the principal 6G visions proposed, the paper tries to establish important motivations and characteristics that can really justify the need for and the novelty of future 6G communication networks.

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Machine Learning for 6G Wireless Networks: Carrying Forward Enhanced Bandwidth, Massive Access, and Ultrareliable/Low-Latency Service

Cited by 260 publications

References 42 publications

Task Offloading and Serving Handover of Vehicular Edge Computing Networks Based on Trajectory Prediction

Task Offloading and Serving Handover of Vehicular Edge Computing Networks Based on Trajectory Prediction

The Road Towards 6G: A Comprehensive Survey

Why do we need 6G?

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