EMM: Energy-Aware Mobility Management for Mobile Edge Computing in Ultra Dense Networks

Sun, Yuxuan; Zhou, Sheng; Xu, Jie

doi:10.1109/jsac.2017.2760160

Cited by 400 publications

(176 citation statements)

References 27 publications

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“…MAB theory has been widely applied in wireless networks, such as power allocation in small base stations [16] [17], content placement in edge caching [18], [19], task assignment in mobile crowdsourcing [20] and mobility management in mobile edge computing [21]. Very recently, the BA problem is studied based on MAB theory, which makes online decision to strike the balance between exploitation and exploration.…”

Section: Related Workmentioning

confidence: 99%

Fast mmwave Beam Alignment via Correlated Bandit Learning

Cheng

Zhang

et al. 2019

IEEE Trans. Wireless Commun.

128

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Beam alignment (BA) is to ensure the transmitter and receiver beams are accurately aligned to establish a reliable communication link in millimeter-wave (mmwave) systems. Existing BA methods search the entire beam space to identify the optimal transmit-receive beam pair, which incurs significant BA latency on the order of seconds in the worst case. In this paper, we develop a learning algorithm to reduce BA latency, namely Hierarchical Beam Alignment (HBA) algorithm. We first formulate the BA problem as a stochastic multi-armed bandit problem with the objective to maximize the cumulative received signal strength within a certain period. The proposed algorithm takes advantage of the correlation structure among beams such that the information from nearby beams is extracted to identify the optimal beam, instead of searching the entire beam space. Furthermore, the prior knowledge on the channel fluctuation is incorporated in the proposed algorithm to further accelerate the BA process.Theoretical analysis indicates that the proposed algorithm is asymptotically optimal. Extensive simulation results demonstrate that the proposed algorithm can identify the optimal beam with a high probability and reduce the BA latency from hundreds of milliseconds to a few milliseconds in the multipath channel, as compared to the existing BA method in IEEE 802.11ad.

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Section: Related Workmentioning

confidence: 99%

Fast mmwave Beam Alignment via Correlated Bandit Learning

Cheng

Zhang

et al. 2019

IEEE Trans. Wireless Commun.

128

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“…Such allocation faces a few critical issues. First, the stochastic nature of wireless channels and task arrivals should be considered [100], [36]; Second, the limited radio and computing resources are shared by multiple users, both of which will affect the computation latency [101], [102]; Finally, the mobility of vehicles will affect the task offloading and result feedback [103].…”

Section: A Vehicle As a Clientmentioning

confidence: 99%

“…Mobility-aware resource management for MEC has also received lots of attention. In [103], an online energy-aware mobility management scheme was developed, accounting for the radio handover and computation migration cost. An effective mobility-aware offloading decision algorithm was proposed in [108], by integrating mobility prediction.…”

Section: A Vehicle As a Clientmentioning

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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“…The classical MAB problem aims at balancing the exploration and exploitation tradeoff in the learning process: to explore different candidate actions that lead to good estimates of their reward distributions, while to exploit the learned information to select the empirically optimal actions. The upper confidence bound (UCB) based algorithms, such as UCB1 and UCB2, have been proposed with strong performance guarantee [15], and applied to the wireless networks to learn the unknown environments [16]- [18]. However, in our task offloading problem, the movements of vehicles lead to a dynamic candidate SeV set, and the workload of each task is time-varying, leading to a varying cost in exploring the suboptimal actions.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Learning-Based Task Offloading for Vehicular Edge Computing Systems

Sun

Guo

Song

et al. 2019

IEEE Trans. Veh. Technol.

Self Cite

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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EMM: Energy-Aware Mobility Management for Mobile Edge Computing in Ultra Dense Networks

Cited by 400 publications

References 27 publications

Fast mmwave Beam Alignment via Correlated Bandit Learning

Fast mmwave Beam Alignment via Correlated Bandit Learning

Mobile Edge Intelligence and Computing for the Internet of Vehicles

Adaptive Learning-Based Task Offloading for Vehicular Edge Computing Systems

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