5G-MiEdge: Design, standardization and deployment of 5G phase II technologies: MEC and mmWaves joint development for Tokyo 2020 Olympic games

Frascolla, Valerio; Miatton, Federico; Tran, Gia Khanh; Takinami, Koji; Domenico, Antonio De; Strinati, Emilio Calvanese; Koslowski, Konstantin; Haustein, Thomas; Sakaguchi, Kei; Barbarossa, Sergio; Barberis, Sergio Daniel

doi:10.1109/cscn.2017.8088598

Cited by 35 publications

(23 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a benefit of its substantial bandwidth and high transmission rate, mmWave techniques are eminently suitable for MEC systems. This idea also meets the vision of the 5G-MiEdge initiative (Millimeter-wave Edge Cloud as an Enabler for 5G Ecosystem) for the forthcoming Tokyo 2020 Olympics [16]. However, compared to the regular sub-5 GHz frequency band, mmWave channels impose a high path-loss, high penetration loss, rain-attenuation, etc [17].…”

Section: Introductionmentioning

confidence: 83%

See 1 more Smart Citation

Mobile Edge Computing Meets mmWave Communications: Joint Beamforming and Resource Allocation for System Delay Minimization

Zhao

Cai

Liu

et al. 2020

IEEE Trans. Wireless Commun.

View full text Add to dashboard Cite

Mobile edge computing (MEC) has been identified as a key technique of next-generation wireless networks, which supports cloud computing along with other compelling service capabilities at the network's edge with the objective of reducing the system delay. As one of the prospective candidates for new spectrum in next-generation networks, millimeter wave (mmWave) communications has been gaining significant attention as a benefit of its high rate. Hence we conceive a joint hybrid beamforming and resource allocation algorithm for mmWave MEC. Explicitly, we jointly optimize the analog beamforming vectors at the users, the analog and digital beamforming matrices at the base station (BS), the computation task offloading ratios and resource allocation at the MEC server for minimizing the maximum system delay subject to the affordable communication and computing budget. We conceive a powerful algorithm for solving this challenging nonconvex optimization problem with coupled constraints based on the penalty dual decomposition (PDD) technique. The proposed algorithm can be implemented in a parallel and distributed fashion. Our numerical results demonstrate the superiority of the proposed algorithm by quantifying the benefits of intrinsically amalgamating MEC with mmWave communications.

show abstract

Section: Introductionmentioning

confidence: 83%

“…By following the same approach, constraints (11c) and (11f) can be approximated as (17) and (18), respectively. Based on the concept of CCCP [33], [34], problem (12) in the lth iteration can be expressed as the following approximated convex one (16), (17), (18). (19b)…”

Section: Proposed Cccp-based Algorithm For Solving Problemmentioning

confidence: 99%

Mobile Edge Computing Meets mmWave Communications: Joint Beamforming and Resource Allocation for System Delay Minimization

Zhao

Cai

Liu

et al. 2020

IEEE Trans. Wireless Commun.

View full text Add to dashboard Cite

show abstract

“…MEC will play a vital role here also. For instance, MEC technologies may fulfill the latency, reliability, and throughput requirements in V2X channel modeling of mmWave communication [65]. Moreover, the placement of the MEC server within the RAN provides flexible network services for the vehicle and to efficiently control the radio network resources [66].…”

Section: Autonomous Vehicles/iot Automotivementioning

confidence: 99%

Survey on Multi-Access Edge Computing for Internet of Things Realization

Porambage

Okwuibe

Liyanage

et al. 2018

IEEE Commun. Surv. Tutorials

647

328

View full text Add to dashboard Cite

The Internet of Things (IoT) has recently advanced from an experimental technology to what will become the backbone of future customer value for both product and service sector businesses. This underscores the cardinal role of IoT on the journey towards the fifth generation (5G) of wireless communication systems. IoT technologies augmented with intelligent and big data analytics are expected to rapidly change the landscape of myriads of application domains ranging from health care to smart cities and industrial automations. The emergence of Multi-Access Edge Computing (MEC) technology aims at extending cloud computing capabilities to the edge of the radio access network, hence providing real-time, high-bandwidth, low-latency access to radio network resources. IoT is identified as a key use case of MEC, given MEC's ability to provide cloud platform and gateway services at the network edge. MEC will inspire the development of myriads of applications and services with demand for ultra low latency and high Quality of Service (QoS) due to its dense geographical distribution and wide support for mobility. MEC is therefore an important enabler of IoT applications and services which require real-time operations. In this survey, we provide a holistic overview on the exploitation of MEC technology for the realization of IoT applications and their synergies. We further discuss the technical aspects of enabling MEC in IoT and provide some insight into various other integration technologies therein.

show abstract

“…Based on the corresponding key performance indicators, they can be widely categorized as the three primary services, that is, enhanced mobile broadband (eMBB), massive machine‐type communications (mMTC), also known as massive Internet of Things (mIoT), and ultrareliable low‐latency communications (uRLLC), also referred to as mission‐critical machine‐type communications (cMTC). In the forthcoming 5G Phase II within the 3rd Generation Partnership Project (3GPP), ultrahigh‐speed low‐latency communications (uHSLLC) 8 are envisioned as a combination of eMBB and uRLLC. Here, performance indicators can be treated as technical requirements for 5G generic services, as presented in Figure 1.…”

Section: Principles Of Network Slicing In 5g Mobile Systemsmentioning

confidence: 99%

TOPSIS‐based approach for network slice selection in 5G mobile systems

Bakmaz

Bojković

Bakmaz

2020

Int J Communication

View full text Add to dashboard Cite

Summary Fifth‐generation (5G) mobile networks can be perceived as highly manageable systems that provide increased performance while supporting a variety of services with widely diverse requirements. Recently, network slicing has been proposed by academia and industry as a resource provisioning technique capable to meet these requirements with reduced operating costs while opening new horizons for network efficiency. The aim of network slice selection function (NSSF) is an optimal selection of network instances serving the users, based on local configuration, and other available information including radio access networks (RANs) performances in the registration area. In this paper, NSSF based on Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is proposed. Here, the network slices are treated as alternatives while their performance indicators are considered as the criteria for decision making. The influences of various alternative techniques, taking into account all the three stages of decision making process, that is, normalization, weighting, and ranking, are analyzed through the rank reversal phenomenon and computational complexity. Simulation results reveal that the proposed techniques can significantly improve the network slice selection procedure.

show abstract

5G-MiEdge: Design, standardization and deployment of 5G phase II technologies: MEC and mmWaves joint development for Tokyo 2020 Olympic games

Cited by 35 publications

References 4 publications

Mobile Edge Computing Meets mmWave Communications: Joint Beamforming and Resource Allocation for System Delay Minimization

Mobile Edge Computing Meets mmWave Communications: Joint Beamforming and Resource Allocation for System Delay Minimization

Survey on Multi-Access Edge Computing for Internet of Things Realization

TOPSIS‐based approach for network slice selection in 5G mobile systems

Contact Info

Product

Resources

About