Vehicular Communications: A Physical Layer Perspective

Liang, Le; Peng, Haibing; Li, Geoffrey Ye; Shen, Xuemin

doi:10.1109/tvt.2017.2750903

Cited by 231 publications

(116 citation statements)

References 115 publications

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“…Taking a one-way straight road with two lanes as an example in Fig. 2, two wireless access technologies, cellular and Wi-Fi/DSRC [21], [22], are available to the AVs. Wi-Fi APs/RSUs and eNBs are uniformly deployed on one side of the road, where the ith Wi-Fi AP and the jth eNB are denoted by W i and S j , respectively.…”

Section: B Dynamic Spectrum Management Frameworkmentioning

confidence: 99%

Spectrum Management for Multi-Access Edge Computing in Autonomous Vehicular Networks

Peng

Shen

2020

IEEE Trans. Intell. Transport. Syst.

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In this paper, a dynamic spectrum management framework is proposed to improve spectrum resource utilization in a multi-access edge computing (MEC) in autonomous vehicular network (AVNET). To support the increasing data traffic and guarantee quality-of-service (QoS), spectrum slicing, spectrum allocating, and transmit power controlling are jointly considered. Accordingly, three non-convex network utility maximization problems are formulated to slice spectrum among BSs, allocate spectrum among autonomous vehicles (AVs) associated with a BS, and control transmit powers of BSs, respectively. Via linear programming relaxation and first-order Taylor series approximation, these problems are transformed into tractable forms and then are jointly solved through an alternate concave search (ACS) algorithm. As a result, optimal spectrum slicing ratios among BSs, optimal BS-vehicle association patterns, optimal fractions of spectrum resources allocated to AVs, and optimal transmit powers of BSs are obtained. Based on our simulation, a high aggregate network utility is achieved by the proposed spectrum management scheme compared with two existing schemes.

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Section: B Dynamic Spectrum Management Frameworkmentioning

confidence: 99%

Spectrum Management for Multi-Access Edge Computing in Autonomous Vehicular Networks

Peng

Shen

2020

IEEE Trans. Intell. Transport. Syst.

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“…2) R2S task assignment delay: For simplicity of system model, we assume transmission between RSU and SeV is operated with a fixed transmission rate r (RS),t by leveraging power/spectrum allocation strategies [27]. However, our algorithm is compatible with other R2S transmission models which do not give transmission rate exactly.…”

Section: B Service Delay and Replication Qualitymentioning

confidence: 99%

“…Now the three components of event E 3 are considered separately. By definition of µ best (p) and µ worst (p) in (26) and (27). The first component of E 3 holds that…”

Section: Appendix B Proof Of Propositionmentioning

confidence: 99%

Task Replication for Vehicular Cloud: Contextual Combinatorial Bandit with Delayed Feedback

Chen

2019

IEEE INFOCOM 2019 - IEEE Conference on Computer Communications

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Vehicular Cloud Computing (VCC) is a new technological shift which exploits the computation and storage resources on vehicles for computational service provisioning. Spare on-board resources are pooled by a VCC operator, e.g. a roadside unit, to serve computational tasks using the vehicleas-a-resource framework. This paper investigates timely service provisioning for deadline-constrained tasks in VCC systems by leveraging the task replication technique (i.e., allowing one task to be executed by vehicles). A learning-based algorithm, called DATE-V (Deadline-Aware Task rEplication for Vehicular Cloud), is proposed to address the special issues in VCC systems including uncertainty of vehicle movements, volatile vehicle members, and large vehicle population. The proposed algorithm is developed based on a novel contextual-combinatorial multi-armed bandit learning framework. DATE-V is "contextual" because it utilizes side information (context) of vehicles and tasks to infer the completion probability of a task replication under random vehicle movements. DATE-V is "combinatorial" because it replicates the received task and sends task replications to multiple vehicles to guarantee the service timeliness. When learning with multi-armed bandit, DATE-V also addresses the practical concern of delayed feedbacks caused by the task transmission/computational delay in using VCC. We rigorously prove that our learning algorithm achieves a sublinear regret bound compared to an oracle algorithm that knows the exact completion probability of any task replications. Simulations are carried out based on real-world vehicle movement traces and the results show that DATE-V significantly outperforms benchmark solutions.

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“…Vehicles can also communicate with the roadside infrastructure via Vehicle-to-Infrastructure (V2I) communications to collect road and traffic-related information. These communication links are enabled by Dedicated Short Range Communications (DSRC) or cellular-enabled Vehicle-to-Everything (V2X) communications [6], [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Mobile Edge Intelligence and Computing for the Internet of Vehicles

2020

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The Internet of Vehicles (IoV) is an emerging paradigm, driven by recent advancements in vehicular communications and networking. Advances in research can now provide reliable communication links between vehicles, via vehicle-to-vehicle communications, and between vehicles and roadside infrastructures, via vehicle-to-infrastructure communications. Meanwhile, the capability and intelligence of vehicles are being rapidly enhanced, and this will have the potential of supporting a plethora of new exciting applications, which will integrate fully autonomous vehicles, the Internet of Things (IoT), and the environment. These trends will bring about an era of intelligent IoV, which will heavily depend upon communications, computing, and data analytics technologies. To store and process the massive amount of data generated by intelligent IoV, onboard processing and Cloud computing will not be sufficient, due to resource/power constraints and communication overhead/latency, respectively. By deploying storage and computing resources at the wireless network edge, e.g., radio access points, the edge information system (EIS), including edge caching, edge computing, and edge AI, will play a key role in the future intelligent IoV. Such system will provide not only low-latency content delivery and computation services, but also localized data acquisition, aggregation and processing. This article surveys the latest development in EIS for intelligent IoV. Key design issues, methodologies and hardware platforms are introduced. In particular, typical use cases for intelligent vehicles are illustrated, including edge-assisted perception, mapping, and localization. In addition, various open research problems are identified.

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Vehicular Communications: A Physical Layer Perspective

Cited by 231 publications

References 115 publications

Spectrum Management for Multi-Access Edge Computing in Autonomous Vehicular Networks

Spectrum Management for Multi-Access Edge Computing in Autonomous Vehicular Networks

Task Replication for Vehicular Cloud: Contextual Combinatorial Bandit with Delayed Feedback

Mobile Edge Intelligence and Computing for the Internet of Vehicles

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