Vehicle-to-Vehicle Communications with Urban Intersection Path Loss Models

Abdulla, Mouhamed; Steinmetz, Erik; Wymeersch, Henk

doi:10.1109/glocomw.2016.7849078

Cited by 37 publications

(19 citation statements)

References 11 publications

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“…Recall that for each SeV, the allocated CPU frequency f t,n to the TaV is a fraction of the maximum CPU frequency, which is randomly distributed from 20%F n to 50%F n . The wireless channel state is modeled by an inverse power law h (u) t,n = h (d) t,n = A 0 l −2 , with A 0 = −17.8dB, and l is the distance between TaV and SeV [33]. Other default parameters include: transmission power P = 0.1W, channel bandwidth W = 10MHz, noise power σ 2 = 10 −13 W, and weight factor β 0 = 0.5.…”

Section: A Simulation Under Synthetic Scenariomentioning

confidence: 99%

Adaptive Learning-Based Task Offloading for Vehicular Edge Computing Systems

Sun

Guo

Song

et al. 2019

IEEE Trans. Veh. Technol.

304

119

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The vehicular edge computing (VEC) system integrates the computing resources of vehicles, and provides computing services for other vehicles and pedestrians with task offloading. However, the vehicular task offloading environment is dynamic and uncertain, with fast varying network topologies, wireless channel states and computing workloads. These uncertainties bring extra challenges to task offloading. In this work, we consider the task offloading among vehicles, and propose a solution that enables vehicles to learn the offloading delay performance of their neighboring vehicles while offloading computation tasks. We design an adaptive learning-based task offloading (ALTO) algorithm based on the multi-armed bandit (MAB) theory, in order to minimize the average offloading delay. ALTO works in a distributed manner without requiring frequent state exchange, and is augmented with input-awareness and occurrence-awareness to adapt to the dynamic environment. The proposed algorithm is proved to have a sublinear learning regret. Extensive simulations are carried out under both synthetic scenario and realistic highway scenario, and results illustrate that the proposed algorithm achieves low delay performance, and decreases the average delay up to 30% compared with the existing upper confidence bound based learning algorithm.Index Terms-Vehicular edge computing, task offloading, online learning, multi-armed bandit.

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Section: A Simulation Under Synthetic Scenariomentioning

confidence: 99%

Adaptive Learning-Based Task Offloading for Vehicular Edge Computing Systems

Sun

Guo

Song

et al. 2019

IEEE Trans. Veh. Technol.

304

119

View full text Add to dashboard Cite

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“…Within the TaV's communication range, the distance between the TaV and each SeV ranges in [10,200]m, and changes randomly by −10m to 10m every time period. According to [22], the wireless channel state is modeled by an inverse power law h (u) t,n = h (d) t,n = A 0 l −2 , where l is the distance between TaV and SeV and A 0 = −17.8dB. Other default parameters are: computation intensity ω 0 = 1000Cycles/bit, transmit power P = 0.1W, channel bandwidth W = 10MHz, noise power σ 2 = 10 −13 W and parameter β in (10) is 2.…”

Section: A Simulation Under Synthetic Scenariomentioning

confidence: 99%

Learning-Based Task Offloading for Vehicular Cloud Computing Systems

Sun

Guo

Zhou

et al. 2018

2018 IEEE International Conference on Communications (ICC)

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Vehicular cloud computing (VCC) is proposed to effectively utilize and share the computing and storage resources on vehicles. However, due to the mobility of vehicles, the network topology, the wireless channel states and the available computing resources vary rapidly and are difficult to predict. In this work, we develop a learning-based task offloading framework using the multi-armed bandit (MAB) theory, which enables vehicles to learn the potential task offloading performance of its neighboring vehicles with excessive computing resources, namely service vehicles (SeVs), and minimizes the average offloading delay. We propose an adaptive volatile upper confidence bound (AVUCB) algorithm and augment it with load-awareness and occurrenceawareness, by redesigning the utility function of the classic MAB algorithms. The proposed AVUCB algorithm can effectively adapt to the dynamic vehicular environment, balance the tradeoff between exploration and exploitation in the learning process, and converge fast to the optimal SeV with theoretical performance guarantee. Simulations under both synthetic scenario and a realistic highway scenario are carried out, showing that the proposed algorithm achieves close-to-optimal delay performance.

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“…After substituting all the expressions of the expectation in (21), (22) and (23), we obtain (24), (25) and (26).…”

Section: Appendix D Proof Of Theoremmentioning

confidence: 99%

“…Steinmetz et al derivate the success probability when the received node and the interferer nodes are aligned on the road [21]. In [22], the authors analyze the performance in terms of success probability for finite road segments under several channel conditions. The authors in [23] evaluate the average and the fine-grained reliability for interference-limited V2V communications with the use of the Meta distribution.…”

mentioning

confidence: 99%

Outage Performance of NOMA at Road Intersections Using Stochastic Geometry

Belmekki

Hamza

Escrig

2019

2019 IEEE Wireless Communications and Networking Conference (WCNC)

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Vehicular safety communications (VSC) are known to provide relevant contributions to avoid congestions and prevent road accidents, and more particularly at road intersections since these areas are more prone to accidents. In this context, one of the main impairments that affect the performance of VSC are interference. In this paper, we develop a tractable framework to model cooperative transmissions in presence of interference for VSC at intersections. We use tools from stochastic geometry, and model interferer vehicles locations as a Poisson point process. First, we calculate the outage probability (OP) for a direct transmission when the received node can be anywhere on the plan. Then, we analyze the OP performance of a cooperative transmission scheme. The analysis takes into account two dimensions:the decoding strategy at the receiver, and the vehicles mobility. We derive the optimal relay position, from analytical and simulation results, for different traffic densities and for different vehicles mobility models. We also show that the OP does not improve after the number of infrastructure relays reached a threshold value. Finally, we show that the OP performance of VSC is higher at intersections than on highways. We validated our analytical results by Monte-Carlo simulations. Index TermsCooperative communications, interference, outage probability, decode-and-forward, stochastic geometry, Poisson point process, vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), intersection. July 24, 2018 DRAFT arXiv:1807.08532v1 [cs.IT] 23 Jul 2018 2 I. INTRODUCTION A-Motivation: Road traffic safety is a major issue, especially at road intersections. Studies showed that 50% of all crashes are in junction roads (intersections) including fatal crashes, injury crashes and property damage crashes [1]. This makes intersections critical areas not only for vehicles, but also for cyclists and pedestrians. Vehicular communications offer a wide range of applications to avoid potential accidents, or to warn vehicles of an accident happening in their vicinity. Vehicular communications consist of vehicle-to-vehicle (V2V) communications, and vehicleto-infrastructure (V2I) communications, in which vehicles interact with infrastructures, e.g. road-side units (RSUs). However, the main limiting factors that can jeopardize V2V and V2I communications, and degrade the performance in terms of outage probability are the interference originated from other transmitting vehicles [2]. Hence, understanding interference dependence is crucial when designing safety and low latency applications and protocols [3]. In order to deal with interference, cooperative communications have been shown to reduce the outage probability, and increase the throughput in the presence of interference [4]. B-Related works: Several works focus on the effect of interference using tools from stochastic geometry, and point process theory. However, few researches focus on interference dependence between interferer nodes and how it affects the performance considering direct t...

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Vehicle-to-Vehicle Communications with Urban Intersection Path Loss Models

Cited by 37 publications

References 11 publications

Adaptive Learning-Based Task Offloading for Vehicular Edge Computing Systems

Adaptive Learning-Based Task Offloading for Vehicular Edge Computing Systems

Learning-Based Task Offloading for Vehicular Cloud Computing Systems

Outage Performance of NOMA at Road Intersections Using Stochastic Geometry

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