Uplink Resource Allocation for Multiple Access Computational Offloading
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The opportunity to offload computational tasks that is provided by the mobile edge computing framework enables mobile users to broaden the range of tasks that they can execute. When multiple users with different requirements seek to offload their tasks, the available communication and computation resources need to be efficiently allocated. The nature of the computational tasks, whether they are divisible or indivisible, and the choice of the multiple access scheme employed by the system have a fundamental impact on the total energy consumption of the offloading users. In this paper, we show that using the full capabilities of the multiple access channel can significantly reduce the energy consumption, and that the required resource allocation can be efficiently computed. In particular, we provide a closed-form optimal solution of the energy minimization problem when a set of users with different latency constraints are completely offloading their computational tasks, and a tailored greedy search algorithm for a good set of users. We also consider "data-partitionable" computational tasks and develop a low-complexity iterative algorithm to find a stationary solution to the energy minimization problem in that case. In addition, we develop low-complexity optimal algorithms for the energy minimization problem under the Time Division Multiple Access (TDMA) scheme in the binary offloading and partial offloading scenarios. Our numerical experiments show that the proposed algorithms outperform existing algorithms in terms of energy consumption and computational cost.
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