Virtual Resource Allocation for Heterogeneous Services in Full Duplex-enabled SCNs with Mobile Edge Computing and Caching
In the area of full duplex (FD)-enabled small cell networks (SCNs), only limited works have been done on the consideration of mobile edge computing (MEC) and caching. In this paper, a virtual FD-enabled small cell network framework with MEC and caching is investigated for two kinds of heterogeneous services, high-data-rate service and computation-sensitive service. In our proposed framework, content caching and FD communication are jointly considered to provide high-data-rate service without the cost of backhaul resource. And computation-sensitive service is offloaded to MEC guaranteeing the delay requirement of users. From the view point of heterogeneous services, we formulate a virtual resource allocation problem, in which quality of experience (QoE) of users and corresponding resource consumption are recognized as system revenue and cost, respectively. Particularly, user association, power control and resources (including spectrum, caching and computing) allocation are jointly considered. Since the optimized problem is non-convex, necessary variable relaxation and reformulation are conducted to transfer the original problem to a convex problem. Furthermore, alternating direction method of multipliers (ADMM) algorithm is adopted to obtain the optimal solution with low computation complexity. Finally, extensive simulations are conducted with different system parameter configurations to verify the effectiveness of our proposed scheme.
|Keywords||Association selection, cache, Computational modeling, full duplex-enabled small cell networks, heterogeneous services, MEC, Microcell networks, Resource management, virtual resource allocation, Virtualization, Wireless networks|
|Journal||IEEE Transactions on Vehicular Technology|
Tan, Z. (Zhiyuan), Yu, F.R, Li, X. (Xi), Ji, H. (Hong), & Leung, V.C. (Victor C.M.). (2017). Virtual Resource Allocation for Heterogeneous Services in Full Duplex-enabled SCNs with Mobile Edge Computing and Caching. IEEE Transactions on Vehicular Technology. doi:10.1109/TVT.2017.2764002