Title
Joint Communication And Computation Resource Optimization In Fd-Mec Cellular Networks
Abstract
In view of the growing contradiction between the intensive computation demands and the resource limitations of mobile users, mobile edge computing (MEC) and simultaneous wireless information and power transfer (SWIPT) have emerged as new paradigms towards 5G communication. However, coordinating the communication and computation between users and edge servers proves to be challenging for MEC. In this paper, we propose a novel multi-user full-duplex (FD) communication system that combines MEC and SWIPT technology in order to take the advantage of high-speed mobile computing and long-lasting self-sustainability. Through MEC technology, users are able to calculate local computation tasks using their batteries, and can offload partial computation tasks to the base station (BS) to reduce their energy shortage. Moreover, users can refill their batteries while receiving the computation result sent by the BS, thus benefiting from SWIPT technology. The FD mode can potentially increase the system performance by allowing the simultaneous transmitting and receiving of computation tasks. Our work aims to minimize the energy consumption of the system, while formulating resource allocation as a joint non-linear optimization problem. We decouple the original non-convex problem into two subproblems and solve them using a proposed algorithm that applies group iterative optimization. Numerical results prove that the proposed algorithm is superior to other two comparison schemes and can significantly reduce the system energy consumption and the latency.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2954622
IEEE ACCESS
Keywords
DocType
Volume
Full-duplex, mobile edge computing, offload, simultaneous wireless information and power transfer, group iterative optimization
Journal
7
ISSN
Citations 
PageRank 
2169-3536
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Fangni Chen131.39
Jiafei Fu200.34
Zhongpeng Wang300.34
Yang Zhou400.34
Weiwei Qiu500.34