Wireless Edge Computing With Latency and Reliability Guarantees

Elbamby, Mohammed S.; Perfecto, Cristina; Liu, Chen-Feng; Park, Jihong; Samarakoon, Sumudu; Chen, Xianfu; Bennis, Mehdi

doi:10.1109/jproc.2019.2917084

Cited by 118 publications

(55 citation statements)

References 89 publications

(95 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Next, with the helper-device split, the device may want to offload its computation to the helper with higher computation power without energy limitation. The bottleneck is the offloading communication overhead, and optimizing such a computation-communication tradeoff is a large field of research in mobile edge computing (MEC) [119]- [122].…”

Section: ) On-device Constraints: A) Energy Limitationmentioning

confidence: 99%

Wireless Network Intelligence at the Edge

et al. 2019

Self Cite

View full text Add to dashboard Cite

edge devices. The new breed of intelligent devices and high-stake applications (drones, augmented/virtual reality, autonomous systems, and so on) requires a novel paradigm change calling for distributed, low-latency and reliable ML at the wireless network edge (referred to as edge ML). In edge ML, training data are unevenly distributed over a large number of edge nodes, which have access to a tiny fraction of the data. Moreover, training and inference are carried out collectively over wireless links, where edge devices communicate and exchange their learned models (not their private data). In a first of its kind, this article explores the key building blocks of edge ML, different neural network architectural splits and their inherent tradeoffs, as well as theoretical and technical enablers stemming from a wide range of mathematical disciplines. Finally, several case studies pertaining to various high-stake applications are presented to demonstrate the effectiveness of edge ML in unlocking the full potential of 5G and beyond.

show abstract

Section: ) On-device Constraints: A) Energy Limitationmentioning

confidence: 99%

Wireless Network Intelligence at the Edge

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…It optimizes communication networks and operations appropriately. One can provide a fresh look to the concept of end node computing by first discussing the applications that the network edge must provide, with a special emphasis on the ensuing challenges in enabling URLLC edge computing services for critical mMTC applications such as VR, V2X, haptics and so forth [81], [104]. Such edge and end node computing paradigm can be integrated with other features (such as software-defined SDNs and NFV) to cope with service disruptions [88], while improving resiliency and reducing latency.…”

Section: B Existing Solutionsmentioning

confidence: 99%

Towards Enabling Critical mMTC: A Review of URLLC Within mMTC

et al. 2020

Self Cite

View full text Add to dashboard Cite

Massive machine-type communication (mMTC) and ultrareliable and low-latency communication (URLLC) are two key service types in the fifth-generation (5G) communication systems, pursuing scalability and reliability with low-latency, respectively. These two extreme services are envisaged to agglomerate together into critical mMTC shortly with emerging use cases (e.g., wide-area disaster monitoring, wireless factory automation), creating new challenges to designing wireless systems beyond 5G. While conventional network slicing is effective in supporting a simple mixture of mMTC and URLLC, it is difficult to simultaneously guarantee the reliability, latency, and scalability requirements of critical mMTC (e.g., < 4ms latency, 106 devices/km 2 for factory automation) with limited radio resources. Furthermore, recently proposed solutions to scalable URLLC (e.g., machine learning aided URLLC for driverless vehicles) are ill-suited to critical mMTC whose machine type users have extremely limited energy budget and computing capability that should be tightly optimized for given tasks. In view of this, this paper aims to characterize promising use cases of critical mMTC and search for their possible solutions. To this end, we first review the state-of-theart (SOTA) technologies for separate mMTC and URLLC services and then identify key challenges from conflicting SOTA requirements, followed by potential approaches to prospective critical mMTC solutions at different layers. Index Terms-Ultra reliable low latency communication (URLLC), massive machine type communications (mMTC), critical mMTC, 5G, beyond 5G.

show abstract

“…Nevertheless, most of those UEs have some limitations like low processing capabilities and weak energy storage battery. These defects would hinder them from accomplishing the task-intensive applications that come with the mentioned applications [2], [3], [5], [7].…”

Section: Introductionmentioning

confidence: 99%

Online Computation Offloading in NOMA-Based Multi-Access Edge Computing: A Deep Reinforcement Learning Approach

2020

View full text Add to dashboard Cite

One of the missions of fifth generation (5G) wireless networks is to provide massive connectivity of the fast growing number of Internet of Things (IoT) devices. To satisfy this mission, nonorthogonal multiple access (NOMA) has been recognized as a promising solution for 5G networks to significantly improve the network capacity. Considered as a booster of IoT devices, and in parallel with the development of NOMA techniques, multi-access edge computing (MEC) is also becoming one of the key emerging technologies for 5G networks. In this paper, with an objective of maximizing the computation rate of an MEC system, we investigate the computation offloading and subcarrier allocation problem in Multicarrier (MC) NOMA based MEC systems and address it using Deep Reinforcement Learning for Online Computation Offloading (DRLOCO-MNM) algorithm. In particular, the DRLOCO-MNM helps each of the user equipments (UEs) decides between local and remote computation modes, and also assigns the appropriate subcarrier to the UEs in the case of remote computation mode. The DRLOCO-MNM algorithm is especially advantageous over the other machine learning techniques applied on NOMA because it does not require labeled data for training or a complete definition of the channel environment. The DRLOCO-MNM also does avoid the complexity found in many optimization algorithms used to solve channel allocation in existing NOMA related studies. Numerical simulations and comparison with other algorithms show that our proposed module and its algorithm considerably improve the computation rates of MEC systems. INDEX TERMS 5G Networks, Deep Reinforcement Learning (DRL), Multi Access Edge Computing (MEC), Non-Orthogonal Multiple Access (NOMA), Online Computation Offloading.

show abstract

Wireless Edge Computing With Latency and Reliability Guarantees

Cited by 118 publications

References 89 publications

Wireless Network Intelligence at the Edge

Wireless Network Intelligence at the Edge

Towards Enabling Critical mMTC: A Review of URLLC Within mMTC

Online Computation Offloading in NOMA-Based Multi-Access Edge Computing: A Deep Reinforcement Learning Approach

Contact Info

Product

Resources

About