Mobile-Edge Computing (MEC) is an emerging paradigm that provides a capillarydistribution of cloud computing capabilities to the edge of the wireless accessnetwork, enabling rich services and applications in close proximity to the endusers. In this article, a MEC enabled multi-cell wireless network is consideredwhere each Base Station (BS) is equipped with a MEC server that can assistmobile users in executing computation-intensive tasks via task offloading. Theproblem of Joint Task Offloading and Resource Allocation (JTORA) is studied inorder to maximize the users' task offloading gains, which is measured by thereduction in task completion time and energy consumption. The consideredproblem is formulated as a Mixed Integer Non-linear Program (MINLP) thatinvolves jointly optimizing the task offloading decision, uplink transmissionpower of mobile users, and computing resource allocation at the MEC servers.Due to the NP-hardness of this problem, solving for optimal solution isdifficult and impractical for a large-scale network. To overcome this drawback,our approach is to decompose the original problem into (i) a ResourceAllocation (RA) problem with fixed task offloading decision and (ii) a TaskOffloading (TO) problem that optimizes the optimal-value function correspondingto the RA problem. We address the RA problem using convex and quasi-convexoptimization techniques, and propose a novel heuristic algorithm to the TOproblem that achieves a suboptimal solution in polynomial time. Numericalsimulation results show that our algorithm performs closely to the optimalsolution and that it significantly improves the users' offloading utility overtraditional approaches.
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