Demonstration of VR / AR offloading to Mobile Edge Cloud for low latency 5G gaming application

Schmoll, Robert-Steve; Pandi, Sreekrishna; Braun, Patrik J.; Fitzek, Frank H. P.

doi:10.1109/ccnc.2018.8319323

Cited by 48 publications

(15 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Computations and the availability of resources are essential for achieving the required spontaneous services. [36] provided a demo that implemented a VR game with the capability of moving game servers across the world without service interruption. AI : Advances in deep learning have allowed very complex algorithms to be applied in data analysing applications. This has been made possible due to the petabytes of data generated by networks and services on the Internet and otherwise.…”

Section: Health Care Applicationsmentioning

confidence: 99%

Survey on health care applications in 5G networks

2020

View full text Add to dashboard Cite

Section: Health Care Applicationsmentioning

confidence: 99%

Survey on health care applications in 5G networks

2020

View full text Add to dashboard Cite

“…The IoT is a network of physical Objects, vehicles, buildings, and other elements—Integrated with electronic devices, software, sensors, and network connection allows these objects to collect and share data. The use of fifth‐generation mobile communications and existing communication systems can make cloud computing more efficient and accurate …”

Section: Introductionmentioning

confidence: 99%

“…The use of fifth-generation mobile communications and existing communication systems can make cloud computing more efficient and accurate. 1,10,11 With the increase in wireless communication specifications for IoT devices and the shrinking of IoT modules, the design space of the antennas can be challenged. Therefore, how to make space for the antenna design has become an increasingly important issue.…”

Section: Introductionmentioning

confidence: 99%

Low‐cost, low‐profile and miniaturized single‐plane antenna design for an Internet of Thing device applications operating in 5G, 4G, V2X, DSRC, WiFi 6 band, WLAN, and WiMAX communication systems

Chung

Chang

2019

Micro & Optical Tech Letters

View full text Add to dashboard Cite

An integrated multi‐band monopole antenna with three radiating strips having a compact structure to provide a double‐wideband operation in an Internet of Things (IoT) device is studied and presented. The proposed antenna is very suitable for the heterogeneous multi‐access network integration trend applications in mobile or fixed IoT devices, which can cover the 4G and 5G operating frequencies, the vehicle‐to‐everything and dedicated short range communication band from the IEEE 802.11p based wireless technology, the WiFi 6 band from the IEEE 802.11ax, the WLAN, and the WiMAX operating frequencies. The proposed antenna is manufactured on a single side of high quality FR4 substrate, which has a compact dimension of 8(L) × 8(W) × 0.8(T) mm3, and is composed of multibranch strips, namely, multiple curved shapes, L shapes to be embedded in the IoT device as an built‐in antenna. There are two operating bands designed between 2355‐5000 and 5112‐7000 MHz with well radiation efficiencies. The measured results show that the two operating bands are satisfied with reflection coefficient better than −6 dB, and total efficiencies of higher than 44% to 83%. In addition to that, the proposed antenna with multiband operation having a single side structure is simple to manufacture, low cost, low profile, and compact advantages.

show abstract

“…It is envisioned that 5G-and-beyond will enable an unprecedented proliferation of data-intensive and computationallyintensive applications such as face recognition, locationbased augmented/virtual reality (AR/VR), and online 3D gaming [1]- [5]. However, adoption of these resource-hungry applications will be negatively affected by limited onboard computing and energy resources.…”

Section: Introductionmentioning

confidence: 99%

Energy-Aware Multi-Server Mobile Edge Computing: A Deep Reinforcement Learning Approach

Naderializadeh¹,

Hashemi

2019

2019 53rd Asilomar Conference on Signals, Systems, and Computers

View full text Add to dashboard Cite

We investigate the problem of computation offloading in a mobile edge computing architecture, where multiple energy-constrained users compete to offload their computational tasks to multiple servers through a shared wireless medium. We propose a multi-agent deep reinforcement learning algorithm, where each server is equipped with an agent, observing the status of its associated users and selecting the best user for offloading at each step. We consider computation time (i.e., task completion time) and system lifetime as two key performance indicators, and we numerically demonstrate that our approach outperforms baseline algorithms in terms of the trade-off between computation time and system lifetime.

show abstract

Demonstration of VR / AR offloading to Mobile Edge Cloud for low latency 5G gaming application

Cited by 48 publications

References 1 publication

Survey on health care applications in 5G networks

Survey on health care applications in 5G networks

Low‐cost, low‐profile and miniaturized single‐plane antenna design for an Internet of Thing device applications operating in 5G, 4G, V2X, DSRC, WiFi 6 band, WLAN, and WiMAX communication systems

Energy-Aware Multi-Server Mobile Edge Computing: A Deep Reinforcement Learning Approach

Contact Info

Product

Resources

About