On the Design of Computation Offloading in Cache-Aided D2D Multicast Networks

被引:22
作者
Wang, Dongyu [1 ]
Lan, Yanwen [1 ]
Zhao, Tiezhu [2 ]
Yin, Zhenping [2 ]
Wang, Xiaoxiang [1 ]
机构
[1] Beijing Univ Posts & Telecommun, Minist Educ, Key Lab Universal Wireless Commun, Beijing 100876, Peoples R China
[2] Samsung R&D Inst China, Beijing 100102, Peoples R China
基金
中国国家自然科学基金;
关键词
D2D Multicast; content offloading; cooperative caching; coded caching; computation offloading; MEC; TO-DEVICE COMMUNICATIONS; RESOURCE-ALLOCATION; ENABLED DEVICE; 5G NETWORKS; MOBILITY; CHALLENGES; SCHEME;
D O I
10.1109/ACCESS.2018.2876893
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
As the demand of data-hungry and computing intensive tasks grows dramatically, cache-aided device-to-device multicast (D2MD) networks are introduced, which can offload traffic from the base stations to D2D users (DUEs) directly to alleviate the heavy burden on backhaul links and improve the energy and spectrum efficiency. However, most previous works ignored the limitation of battery power and scarce computing capabilities of DUEs. In this paper, we study the computation and traffic offloading in cache-aided D2MD networks for the content delivery and delay sensitive task offloading services. Firstly, in order to provide stable multicast links and enhanced computing resources, a D2D cluster head (DCH) selection strategy is proposed that jointly considers the social attributes, available energy, and transfer rate of DUEs. Secondly, to improve the effciency of content distribution and optimize the energy consumption of content delivery, we propose a novel multicast-aware coded and cooperative caching scheme, which may increase the opportunity for D2D multicasting to obtain the desired contents. Thirdly, considering the DUEs association, uplink full duplex DCH transmission power allocation, and mobile edge computing computation resource scheduling, an optimization computation offloading model is formulated. On this basis, we model the computation offloading and resource allocation optimization problem. Furthermore, we transform this problem into user allocation optimization problem and resource allocation optimization problem (RAOP), and RAOP is proved as a convex problem, and the optimal resource allocation solution is found. Finally, the simulation results show that our proposed schemes can effectively decrease the energy consumption and computing costs.
引用
收藏
页码:63426 / 63441
页数:16
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