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作者机构:McMaster Univ Dept Elect & Comp Engn Hamilton ON L8S 4L8 Canada Chongqing Univ Posts & Telecommun Key Lab Mobile Commun Technol Chongqing 400065 Peoples R China
出 版 物:《IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING》 (IEEE Trans. Green Commun. Networking)
年 卷 期:2020年第4卷第3期
页 面:745-758页
核心收录:
基 金:Natural Sciences and Engineering Research Council of Canada National Natural Science Foundation of China (NSFC)
主 题:Small cell cellular networks cloudlets mobile computation offloading radio resource allocations
摘 要:Mobile computation offloading (MCO) is a technique that can help reduce energy consumption of mobile devices (MDs) by offloading their tasks to more powerful devices for execution. In MCO, the offloading decision for a task depends on availability of both communication and computation resource. In small cell cellular networks, cloudlet servers are usually co-located with the small base stations (SBSs). As a result, offloading decisions of the MDs are coupled with SBS associations, while strong overlapping coverage between the SBSs can result in complicated interference conditions in wireless transmissions that affect the offloading performance. In this paper, offloading decisions and SBS associations are jointly optimized with transmission power and channel assignments in a small cell cellular network. The objective is to minimize the total energy consumption of all MDs, subject to task s latency constraints. The problem is first formulated as a mixed binary nonlinear programming problem, then transformed and solved using the general bender decomposition (GBD). A heuristic solution is proposed that recursively allows more MDs to make offloading decisions based on the current transmission conditions. Compared to using GBD, this solution results in much lower worst-case complexity, while achieving good energy performance for a wide range of system parameters.