Communication-and-Computing Latency Minimization for UAV-Enabled Virtual Reality Delivery Systems

Zhou, Yi; Pan, Cunhua; Yeoh, Phee Lep; Wang, Kezhi; Elkashlan, Maged; Vucetic, Branka; Li, Yonghui

doi:10.1109/tcomm.2020.3040283

Cited by 54 publications

(25 citation statements)

References 27 publications

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“…VideoStorm [21] jointly optimizes resources and quality in the video analytics system. [22] designs an algorithm to minimize the maximum communication and computation latency among all VR users under the limited computation resource and transmission power. [23] proposes a joint resolution and power control scheme considering energy efficiency, latency and accuracy trade-off in the MAR system.…”

Section: Related Workmentioning

confidence: 99%

Resource Allocation of Federated Learning for the Metaverse with Mobile Augmented Reality

Zhou¹,

Liu²,

Zhao³

2022

Preprint

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The Metaverse has received much attention recently. Metaverse applications via mobile augmented reality (MAR) require rapid and accurate object detection to mix digital data with the real world. Federated learning (FL) is an intriguing distributed machine learning approach due to its privacy-preserving characteristics. Due to privacy concerns and the limited computation resources on mobile devices, we incorporate FL into MAR systems of the Metaverse to train a model cooperatively. Besides, to balance the trade-off between energy, execution latency and model accuracy, thereby accommodating different demands and application scenarios, we formulate an optimization problem to minimize a weighted combination of total energy consumption, completion time and model accuracy. Through decomposing the non-convex optimization problem into two subproblems, we devise a resource allocation algorithm to determine the bandwidth allocation, transmission power, CPU frequency and video frame resolution for each participating device. We further present the convergence analysis and computational complexity of the proposed algorithm. Numerical results show that our proposed algorithm has better performance (in terms of energy consumption, completion time and model accuracy) under different weight parameters compared to existing benchmarks.

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Section: Related Workmentioning

confidence: 99%

Resource Allocation of Federated Learning for the Metaverse with Mobile Augmented Reality

Zhou¹,

Liu²,

Zhao³

2022

Preprint

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“…Latency also occurs when the server executes the computation task remotely and broadcasts the processed results to the users in the downlink. Consequently, aiming to minimize the maximum latency among users, [52] investigated a joint problem of two-dimensional LAC placement, fronthaul and backhaul bandwidth allocation, computing resource allocation, and caching decision. Further, in [53], the joint optimization of task allocation, scheduling, power control, and LAC server trajectory was studied to minimize the total energy consumption.…”

Section: Network Designmentioning

confidence: 99%

Aerial Computing: A New Computing Paradigm, Applications, and Challenges

Pham,

Ruby,

Fang

et al. 2022

Preprint

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In existing computing systems, such as edge computing and cloud computing, several emerging applications and practical scenarios are mostly unavailable or only partially implemented. To overcome the limitations that restrict such applications, the development of a comprehensive computing paradigm has garnered attention in both academia and industry. However, a gap exists in the literature owing to the scarce research, and a comprehensive computing paradigm is yet to be systematically designed and reviewed. This study introduces a novel concept, called aerial computing, via the amalgamation of aerial radio access networks and edge computing, which attempts to bridge the gap. Specifically, first, we propose a novel comprehensive computing architecture that is composed of low-altitude computing, high-altitude computing, and satellite computing platforms, along with conventional computing systems. We determine that aerial computing offers several desirable attributes: global computing service, better mobility, higher scalability and availability, and simultaneity. Second, we comprehensively discuss key technologies that facilitate aerial computing, including energy refilling, edge computing, network softwarization, frequency spectrum, multiaccess techniques, artificial intelligence, and big data. In addition, we discuss vertical domain applications (e.g., smart cities, smart vehicles, smart factories, and smart grids) supported by aerial computing. Finally, we highlight several challenges that need to be addressed and their possible solutions.

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“…Hammouti et al [18] designed the joint users-UAVs matching, 2D placement, and dynamic altitude adjustment of UAVs, for the purpose of maximizing the system sum rate with the quality of service guaranteed. Zhou et al [19] presented a UAV-enabled communication, computing and caching virtual reality (VR) delivery system, and jointly optimized UAV location, backhaul and fronthaul bandwidth, computing capacity, caching and computing policies. Sun et al [20] proposed a two-tier network architecture, where access points (APs) collected packets from served IoT devices and delivered aggregated data to the UAV, and then studied the UAV's trajectory design and resource allocation.…”

Section: Related Workmentioning

confidence: 99%

Energy-Constrained Computation Offloading in Space-Air-Ground Integrated Networks using Distributionally Robust Optimization

Chen¹,

Ai²,

Niu³

et al. 2022

Preprint

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With the rapid development of connecting massive devices to the Internet, especially for remote areas without cellular network infrastructures, space-air-ground integrated networks (SAGINs) emerge and offload computation-intensive tasks. In this paper, we consider a SAGIN, where multiple low-earthorbit (LEO) satellites providing connections to the cloud server, an unmanned aerial vehicle (UAV), and nearby base stations (BSs) providing edge computing services are included. The UAV flies along a fixed trajectory to collect tasks generated by Internet of Things (IoT) devices, and forwards these tasks to a BS or the cloud server for further processing. To facilitate efficient processing, the UAV needs to decide where to offload as well as the proportion of offloaded tasks. However, in practice, due to the variability of environment and actual demand, the amount of arrival tasks is uncertain. If the deterministic optimization is utilized to develop offloading strategy, unnecessary system

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Communication-and-Computing Latency Minimization for UAV-Enabled Virtual Reality Delivery Systems

Cited by 54 publications

References 27 publications

Resource Allocation of Federated Learning for the Metaverse with Mobile Augmented Reality

Resource Allocation of Federated Learning for the Metaverse with Mobile Augmented Reality

Aerial Computing: A New Computing Paradigm, Applications, and Challenges

Energy-Constrained Computation Offloading in Space-Air-Ground Integrated Networks using Distributionally Robust Optimization

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