Wireless Networks for Mobile Edge Computing: Spatial Modeling and Latency Analysis

Ko, Seung-Woo; Han, Kaifeng; Huang, Kaibin

doi:10.1109/twc.2018.2840120

Cited by 141 publications

(73 citation statements)

References 41 publications

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“…How to improve EE of mobile devices in MEC systems subject to the delay constraint has been widely studied in existing literature [7,8,[12][13][14]. To study the tradeoff between EE and latency, a weighted sum of energy consumption and latency was minimized in a single-AP scenario [7].…”

Section: Related Workmentioning

confidence: 99%

“…EE was maximized subject to the delay constraint in single-AP scenarios and multi-AP scenarios in [8] and [12], respectively. The authors of [13] analyzed the EE and latency with stochastic geometry and provided some useful guidelines to network provision and planning. The above studies mainly focused on one kind of services, and neglected the heterogeneities of services.…”

Section: Related Workmentioning

confidence: 99%

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Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn From a Digital Twin

Dong

She

Hardjawana

et al. 2019

IEEE Trans. Wireless Commun.

204

126

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In this work, we consider a mobile edge computing system with both ultra-reliable and lowlatency communications services and delay tolerant services. We aim to minimize the normalized energy consumption, defined as the energy consumption per bit, by optimizing user association, resource allocation, and offloading probabilities subject to the quality-of-service requirements. The user association is managed by the mobility management entity (MME), while resource allocation and offloading probabilities are determined by each access point (AP). We propose a deep learning (DL) architecture, where a digital twin of the real network environment is used to train the DL algorithm off-line at a central server. From the pre-trained deep neural network (DNN), the MME can obtain user association scheme in a real-time manner. Considering that real networks are not static, the digital twin monitors the variation of real networks and updates the DNN accordingly. For a given user association scheme, we propose an optimization algorithm to find the optimal resource allocation and offloading probabilities at each AP. Simulation results show that our method can achieve lower normalized energy consumption with less computation complexity compared with an existing method and approach to the performance of the global optimal solution.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn From a Digital Twin

Dong

She

Hardjawana

et al. 2019

IEEE Trans. Wireless Commun.

204

126

View full text Add to dashboard Cite

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“…Each AP may serve multiple devices. The aggregation of multiple Bernoulli processes can be modeled as a Poisson process [39]. Thus, the MEC server can be characterized by an M/G/1/PS model, where "M" means the packet arrival process is Poisson process and "G" means that the number of CPU cycles required to process the packets can follow any distributions.…”

Section: A Processing Delay and Delay Violation Probabilitymentioning

confidence: 99%

“…The offloading schemes for URLLC in MEC systems were optimized in [38], where a weighted sum of E2E delay and the offloading failure probability was minimized in a single-user scenario. How to analyze latency in largescale MEC networks was studied in [39], where the average communication and computing latencies were derived.Most of the existing studies on resource management in MEC systems only analyzed UL transmission and processing delay, and assumed DL transmission can be finished with high transmit power at the APs [36][37][38][39]. Besides, they did not take the decoding errors in the short blocklength regime into account, which is crucial for MC-IoT.…”

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confidence: 99%

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Cross-Layer Design for Mission-Critical IoT in Mobile Edge Computing Systems

She

Duan

Zhao

et al. 2019

IEEE Internet Things J.

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In this work, we propose a cross-layer framework for optimizing user association, packet offloading rates, and bandwidth allocation for Mission-Critical Internet-of-Things (MC-IoT) services with short packets in Mobile Edge Computing (MEC) systems, where enhanced Mobile BroadBand (eMBB) services with long packets are considered as background services. To reduce communication delay, the 5th generation new radio is adopted in radio access networks. To avoid long queueing delay for short packets from MC-IoT, Processor-Sharing (PS) servers are deployed at MEC systems, where the service rate of the server is equally allocated to all the packets in the buffer. We derive the distribution of latency experienced by short packets in closed-form, and minimize the overall packet loss probability subject to the end-to-end delay requirement. To solve the non-convex optimization problem, we propose an algorithm that converges to a near optimal solution when the throughput of eMBB services is much higher than MC-IoT services, and extend it into more general scenarios. Furthermore, we derive the optimal solutions in two asymptotic cases: communication or computing is the bottleneck of reliability. Simulation and numerical results validate our analysis and show that the PS server outperforms first-come-first-serve servers. .sg). reliability. Considering that the feedback of Channel State Information (CSI) leads to extra delay, the studies in [19] jointly optimized Uplink (UL) and Downlink (DL) resource configurations without CSI at the transmitters. More recently, how to optimize resource allocation among multiple users with different packet arrival processes was studied in [20].Scheduling policies in computing systems have significant impacts on the Quality-of-Service (QoS) of MC-IoT. A nearoptimal policy to minimize the average latency of short packet is the Shortest Remaining Processing Time (SRPT) first scheduler. Such a scheduler is hard to implement in practice since the remaining processing time is not available at the server, and it requires too many priority levels [21]. To reduce the latency of short packets without introducing priority levels, the Processor-Sharing (PS) server is a possible solution, where the total service rate is equally allocated to all the packets in the server [22]. Although the distribution of latency was derived in the large delay regime in the PS server [23], the latency experienced by short packets remains unclear. To derive the delay bound violation probability of URLLC services, martingales-based analysis, effective capacity, and network calculus were used in [24], [15], and [25], respectively. But all the results were obtained in the FCFS servers. Note that it is very challenging to derive the closed-form expression of the distribution of delay, how to formulate the constraints on delay and reliability of MC-IoT is still unclear.Promising network architectures for MC-IoT were studied in [26][27][28][29][30]. A comprehensive overview on MC-IoT of industrial scenarios was carried out in [26], ...

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Privacy‐preserving cooperative localization in vehicular edge computing infrastructure

Shit

Sharma

Watters

et al. 2020

Concurrency and Computation

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Advancement of computing and communication techniques transforms the traditional transport system into the intelligent transportation system (ITS). The development of distributed computing in a vehicular network platform also called Vehicular Edge Computing (VEC) promise to address most of the challenges faced by the ITS. Localization is important in these vehicular networks because of its key contribution in autonomous driving, smart traffic monitoring, and collision avoidance services. For localization, current GPS and hybrid methods are in-efficient because of GPS outage in urban infrastructure and dynamic nature of the vehicular networks. The cooperative localization approaches, on the other hand, use dedicated short range communication to broadcast messages and estimate location. However, these messages are un-encrypted and periodic which gives a privacy risk for vehicles. This article presents a privacy-preserving cooperative localization in vehicular network based upon dynamic pseudonym changing strategy. First, the localization delay is addressed with the implementation of dynamic vehicular edge assignment for computational task management.In the next step, the localization is estimated from the neighbor and road side unit ranging measurement followed by a real-time prediction of the vehicle. The performance of the proposed algorithms is analyzed in terms of localization accuracy and privacy preservation strength. Furthermore, the proposed method is simulated in a real city scenario followed by localization accuracy and privacy analysis. Finally, the localization accuracy and privacy strength of the proposed approach are compared with the state-of-the-art methods.

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Wireless Networks for Mobile Edge Computing: Spatial Modeling and Latency Analysis

Cited by 141 publications

References 41 publications

Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn From a Digital Twin

Deep Learning for Hybrid 5G Services in Mobile Edge Computing Systems: Learn From a Digital Twin

Cross-Layer Design for Mission-Critical IoT in Mobile Edge Computing Systems

Privacy‐preserving cooperative localization in vehicular edge computing infrastructure

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