Modeling and Performance Analysis of Stochastic Mobile Edge Computing Wireless Networks

Gu, Yixiao; Li, Cheng; Xia, Bin; Xu, Dingjie; Chen, Zhiyong

doi:10.1109/vtcspring.2019.8746583

Cited by 5 publications

(4 citation statements)

References 17 publications

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“…There have been several studies on stochastic geometry-based analysis of MECenabled networks. In particular, single-tier MEC networks were considered in [25,26,27], while heterogeneous MEC networks were considered in [28,29]. Mobile users with identical computation tasks were considered in [26] and [27], with a spatial model characterized by Poisson point processes (PPPs) and Poisson cluster processes (PCPs), respectively.…”

Section: Stochastic Geometry-based Analysismentioning

confidence: 99%

“…In particular, single-tier MEC networks were considered in [25,26,27], while heterogeneous MEC networks were considered in [28,29]. Mobile users with identical computation tasks were considered in [26] and [27], with a spatial model characterized by Poisson point processes (PPPs) and Poisson cluster processes (PCPs), respectively. Although the heterogeneity of computation tasks was considered in [28] and [29], the heterogeneity of computation services was not captured.…”

Section: Stochastic Geometry-based Analysismentioning

confidence: 99%

See 1 more Smart Citation

Computation Offloading and Service Caching in Heterogeneous MEC Wireless Networks

Zhang

Kishk

Alouini

2023

IEEE Trans. on Mobile Comput.

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Section: Stochastic Geometry-based Analysismentioning

confidence: 99%

Section: Stochastic Geometry-based Analysismentioning

confidence: 99%

Computation Offloading and Service Caching in Heterogeneous MEC Wireless Networks

Zhang

Kishk

Alouini

2023

IEEE Trans. on Mobile Comput.

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“…The SWIPT-based MEC networks in this paper within a single base station in areas of communication paralysis are shown in Figure 1. Its service range is R MEC = ½1 km × 1 km [37], and divide R MEC into M parts; each part is assigned a UAV for cruise work, defined as R MEC m . This network consists of one MEC server, N GNs, and M UAVs.…”

Section: System Model and Problem Formulationmentioning

confidence: 99%

Cluster Design and Optimization of SWIPT‐Based MEC Networks with UAV Assistance

Xuefei

Huang

2021

Wireless Communications and Mobile Computing

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In recent years, service isolation and service miniaturization have become very popular. The large services are dismantled into multiple low-cost and simple small services to improve the scalability and disaster tolerance of the entire services. A service network composed of unmanned aerial vehicles (UAVs) and MEC servers is proposed in this paper, which aims at decoupling multiple services of the SWIPT-MEC network. In this network, UAVs take charge of energy transmission and calculation task scheduling and MEC servers are focused on task calculation. To meet the resource requirements of the ground nodes (GNs) in the network, we designed a distributed iterative algorithm to solve the resource allocation decision problem of GNs and used the modified expert bat algorithm to complete the UAV’s trajectory planning in a two-dimensional space. The results show that the algorithm can formulate a more fair resource allocation strategy, and its performance is improved by 7% compared with the traditional bat algorithm. In addition, the algorithm in this paper can also adapt to changes in task length and has a certain degree of stability.

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“…In order to capture the network random traffic, the relationship between the spatial location of base stations (BSs)/access points (APs) and their temporal traffic dynamic was captured in small cell networks [1], [9], [10], heterogeneous cellular networks [11], IoT enabled cellular networks [12], [13], Poisson network [2], [14] and multi-cell mobile edge computing enabled stochastic wireless networks [15]. While the introduction of randomness in the temporal domain further complicates network analysis, relaxing the full buffer assumption is crucial to next generation wireless systems [1].…”

Section: A Related Workmentioning

confidence: 99%

Spatiotemporal Characterization of Users’ Experience in Massive Cognitive Radio Networks

2020

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The need to capture the actual network traffic condition and fundamental queueing dynamics in a massive cognitive radio network (CRN) is important for proper analysis of the intrinsic effects of spatial distribution while capturing the essential temporal distribution properties of the network. In massive CRN, many users, including primary and secondary users, transmit on scarce spectrum resources. While primary users (PUs) are delay-sensitive users that require prioritized access over secondary users (SUs), carrying out analysis that captures this property becomes imperative if users' service experience is to be satisfactory. This paper presents priority conscious spatiotemporal analysis capable of characterizing users' experience in massive CRN. Users in the primary priority queue were considered to have pre-emptive priory over users in the virtual and secondary priority queues. A Geo/G/1 discrete-time Markov chain queueing system was adopted to characterize both primary and secondary priority queues, while the virtual priority queue was analyzed as part of the secondary priority queue. Using the tools of stochastic geometry and queueing theory, the user's coverage probability was determined while the delay experienced by each class of users in the network was obtained using existing results. Through the obtained delay for each class of users in the network, the corresponding quality of service was also obtained. The results obtained show that the proposed framework is capable of accurately characterizing users' service experience in massive CRN.

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Modeling and Performance Analysis of Stochastic Mobile Edge Computing Wireless Networks

Cited by 5 publications

References 17 publications

Computation Offloading and Service Caching in Heterogeneous MEC Wireless Networks

Computation Offloading and Service Caching in Heterogeneous MEC Wireless Networks

Cluster Design and Optimization of SWIPT‐Based MEC Networks with UAV Assistance

Spatiotemporal Characterization of Users’ Experience in Massive Cognitive Radio Networks

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