期刊
IEEE TRANSACTIONS ON SERVICES COMPUTING
卷 14, 期 6, 页码 2011-2025出版社
IEEE COMPUTER SOC
DOI: 10.1109/TSC.2019.2892428
关键词
Task analysis; Resource management; MIMO communication; Optimization; Cloud computing; Estimation; Batteries; Mobile edge-cloud computing; resource allocation; power allocation; computation offloading; MIMO
资金
- Vietnam National University HoChiMinh City (VNU-HCM) [B2018-26-01]
This paper investigates computation task offloading and resource allocation optimization in MIMO based mobile edge computing systems, proposing optimal and low-complexity algorithms to solve mixed integer non-linear problems. Bisection search is used to find the optimal solution for perfect-CSI, while the optimization problem is decomposed into subproblems and iteratively solved in imperfect-CSI scenario using difference of convex functions method.
Intelligent offloading of computation-intensive tasks to a mobile cloud server provides an effective mean to expand the usability of wireless devices and prolong their battery life, especially for low-cost internet-of-things (IoT) devices. However, realization of this technology in multiple-input multiple-output (MIMO) systems requires sophisticated design of joint computation offloading and other network functions such as channel state information (CSI) estimation, beamforming, and resource allocation. In this paper, we study the computation task offloading and resource allocation optimization in MIMO based mobile edge computing systems considering perfect/imperfect-CSI estimation. Our design aims to minimize the maximum weighted energy consumption subject to practical constraints on available computing and radio resources and allowable latency. The optimal and low-complexity algorithms are proposed to solve the underlying mixed integer non-linear problems (MINLP). For the perfect-CSI, we employ bisection search to find the optimal solution. The low-complexity algorithms are developed by decomposing the original optimization problem into the offloading optimization (OP) and power allocation (PA) subproblems and solve them iteratively. Moreover, the difference of convex functions (DC) method is employed to deal with non-convex structure of (PA) subproblems in the imperfect-CSI scenario. Numerical results confirm the advantages of proposed designs over conventional local computation strategies in energy saving and fairness.
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