.png){kind=icon}
.png){kind=icon}
Return
Coded Caching With Device Computing in Mobile Edge Computing Systems
.png){kind=icon}
OA
AI
DOI：10.1109/TWC.2021.3088892.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Edge caching and computing have been regarded as an efficient approach to tackle the wireless spectrum crunch problem. In this paper, we design a general coded caching with device computing strategy for computational tasks, e.g., virtual reality (VR) rendering, to minimize the average transmission bandwidth under the quality of service guarantee. Because both coded data and stored data can be the data before or after computing, the proposed scheme has numerous edge computing and caching paths corresponding to different bandwidth requirement. We thus formulate a joint coded caching and computing optimization problem to decide whether a mobile device stores the data before computing or the data after computing, which tasks to be coded cached and which tasks to be computed locally. The optimization problem is shown to be 0-1 non-convex non-smooth programming, and can be decomposed into a computation offloading programming and a coded caching programming. For a computation offloading subproblem, we proposed an algorithm which applies the convergence of the alternating direction method of multipliers (ADMM) under a non-convex programming and the wide application of the concave-convex procedure (CCCP) for difference of convex (DC) programming to obtain a stationary point, and numerical results verify the convergence of the proposed algorithm and its suboptimality. For the coded cache programming, we design a low complexity algorithm to obtain an acceptable solution. Numerical results demonstrate that the proposed scheme provides a significant bandwidth saving by taking full advantage of the caching and computing capability of mobile devices.
Keywords:
Task analysis
Bandwidth
Programming
Edge computing
Servers
Mobile handsets
Computational modeling
Coded caching
mobile edge computing
multicast
bandwidth allocation
virtual reality
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
10.7
Papers:
1.3W
Citations:
5.3W