Development of MEC system for indigenous 5G Test-Bed

Kherani, Arzad A.; Shukla, Gaurav; Sanadhya, Shashvat; Vasudev, Neha; Ahmed, Muneeb; Patel, Ashish Singh; Mehrotra, Rashi; Lall, Brejesh; Saran, Huzur; Vutukuru, Mythili; Singh, Abhishek Kumar; Seshasayee, Sushila; Viswakumar, Vinodh R; Loganathan, Kishore

doi:10.1109/comsnets51098.2021.9352907

Cited by 13 publications

(3 citation statements)

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“…MEC is one of the most critical pieces of 5G key technology. Deploying MEC close to the terminal enables low latency and high efficiency of services [27]. Besides, MEC also has excellent computing and storage capabilities [28].…”

Section: Smart Mecmentioning

confidence: 99%

Mobile 5G Network Deployment Scheme on High-Speed Railway

2023

Teh. vjesn.

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The fifth-generation (5G) wireless communication has experienced an upsurge of interest for empowering vertical industries, due to its high data volume, extremely low latency, high reliability, and significant improvement in user experience. Specifically, deploying 5G on high-speed railway (HSR) is critical for the promotion of smart travelling such that passengers can connect to the Internet and utilize the on-board time to continue their usual activities. However, there remains a series of challenges in practical implementation, such as the serious Doppler shift caused by the high mobility, the carriage penetration loss especially in the high-frequency bands, frequent handovers, and economic issues. To address these challenges, we propose three schemes in this article to improve the coverage of 5G networks on the train. In particular, we provide a comprehensive description of each scheme in terms of their network architecture and service establishment procedures. Specifically, the mobile edge computing (MEC) is used as the key technology to provide low-latency services for on-board passengers. Moreover, these three schemes are compared among themselves regarding the quality-of-service, the scalability of service, and the related industry development status. Finally, we discuss various potential research directions and open issues in terms of deploying 5G networks on HSR.

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Section: Smart Mecmentioning

confidence: 99%

Mobile 5G Network Deployment Scheme on High-Speed Railway

2023

Teh. vjesn.

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“…Besides, Multi-access Edge Computing (MEC) technology has been widely employed [13], [14] for SFC. In MEC, VNFrelated service requests could be responded at edge with lower latency than the cloud computing model [16], [17], which greatly improves the quality of service (QoS) of IoT applications [15].…”

Section: Introductionmentioning

confidence: 99%

To Deploy New or to Deploy More?: An Online SFC Deployment Scheme at Network Edge

Yuan

Luo

Guo

et al. 2024

IEEE Internet Things J.

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Service Function Chaining (SFC) dynamically links multiple Virtual Network Functions (VNFs) to provide flexible and scalable network services for network entities and users. Implementing SFCs at the network edge provides instant VNF service yet is confined by the limited edge resources. Existing strategies suggest either to deploy new VNFs for diverse service provision or to deploy more installed VNFs for reliable service provision. However, these one-sided optimizations fail to realize comprehensive improvements in the network service quality. To this end, the motivation of this paper is to consider a more comprehensive SFC deployment plan to provide more efficient network services. In this paper, we propose DeepSFC, an online SFC deployment scheme at network edge. Our DeepSFC considers the impact of resource allocations and deployment locations on the average latency of overall service requests. It realizes an elegant trade-off between the diversity and the availability of SFCs by adopting the Deep Reinforcement Learning (DRL) method. To be specific, we first determine the type and number of VNFs that need to be deployed. Thereafter, we optimize the deployment locations of these chosen VNFs in the service chain, considering the impact of dynamic bandwidth in the real network. For more general scenarios wherein users' service requirements change or the deployed server crashes, we further relocate the VNF deployment with the joint consideration of performance degradation and migration cost. Evaluation results show that DeepSFC outperforms its competitors in various experimental settings and responds the requests with lower average latency.

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“…In Ref. [32], the author demonstrates a well‐established MEC architecture and integrates an application deployment use case, establishing a proof of concept, which is very similar to the actual deployment of the MEC system in a 5G environment. Combining with the optimising method, MEC is an appropriate solution to improve the quality of service.…”

Section: Introductionmentioning

confidence: 99%

Unmanned aerial vehicle‐aided edge networks with ultra‐reliable low‐latency communications: A digital twin approach

Huynh

Do‐Duy

et al. 2022

IET Signal Processing

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A digital twin (DT) framework for Internet‐of‐thing (IoT) networks is proposed where unmanned aerial vehicles (UAVs) acting as flying mobile edge computing (MEC) servers support the task offloading on the fly. The considered DT model is very well suitable for industrial automation with the strict constraints of mission‐critical services' ultra‐reliable low‐latency communication (URLLC) links. To support low‐latency IoT devices, we formulate the end‐to‐end (e2e) latency minimisation problem of digital twin‐aided offloading UAV‐URLLC. Specifically, the minimised latency is obtained by jointly optimising both communication and computation parameters, namely power, offloading factors, and the processing rate of IoT devices and MEC‐UAV servers. Due to the highly non‐convex optimisation problem, we first consider the K‐means clustering algorithm to optimally deploy the on‐demand UAVs. Then, an alternative optimisation approach combined with appropriate inner approximations is effectively exploited to tackle this challenge. We demonstrate the effectiveness of the proposed DT framework through representative numerical results.

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Development of MEC system for indigenous 5G Test-Bed

Cited by 13 publications

References 0 publications

Mobile 5G Network Deployment Scheme on High-Speed Railway

Mobile 5G Network Deployment Scheme on High-Speed Railway

To Deploy New or to Deploy More?: An Online SFC Deployment Scheme at Network Edge

Unmanned aerial vehicle‐aided edge networks with ultra‐reliable low‐latency communications: A digital twin approach

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