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Cost Minimization for Cooperative Computation Framework in MEC Networks

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Document pages: 30 pages

Abstract: In this paper, a cooperative task computation framework exploits thecomputation resource in UEs to accomplish more tasks meanwhile minimizes thepower consumption of UEs. The system cost includes the cost of UEs powerconsumption and the penalty of unaccomplished tasks and the system cost isminimized by jointly optimizing binary offloading decisions, the computationalfrequencies, and the offloading transmit power. To solve the formulatedmixed-integer non-linear programming problem, three efficient algorithms areproposed, i.e., integer constraints relaxation-based iterative algorithm(ICRBI), heuristic matching algorithm, and the decentralized algorithm. TheICRBI algorithm achieves the best performance at the cost of the highestcomplexity, while the heuristic matching algorithm significantly reduces thecomplexity while still providing reasonable performance. As the previous twoalgorithms are centralized, the decentralized algorithm is also provided tofurther reduce the complexity, and it is suitable for the scenarios that cannotprovide the central controller. The simulation results are provided to validatethe performance gain in terms of the total system cost obtained by the proposedcooperative computation framework.

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