Joint Power Control and Computation Offloading for Energy-Efficient Mobile Edge Networks

• Published in: IEEE transactions on wireless communications 2022-06, Vol.21 (6), p.4522-4534
• Main Authors: Wu, Fan, Leng, Supeng, Maharjan, Sabita, Huang, Xiaoyan, Zhang, Yan
• Format: Article
• Language: English
• Subjects: Algorithms; Budgets; Cochannel interference; Computation offloading; Computational modeling; Decision analysis; Decision making; Edge computing; Electronic devices; Energy consumption; Heuristic methods; invex; joint heterogeneous resource allocation; Linear equations; Mathematical analysis; Mobile computing; Mobile edge computing; Mobile handsets; Networks; Optimization; Power control; Power management; Resource allocation; Resource management; Servers; Task analysis
• ISSN: 1536-1276; 1558-2248
• DOI: 10.1109/TWC.2021.3130649

Energy saving for mobile devices is considered to be one of prospective benefits of mobile edge computing (MEC) networks, where computation-intensive tasks can be offloaded from the mobile devices to their associated MEC servers for execution. Extra energy consumption for data migration should therefore be less than the energy consumption for local execution. However, in multi-cell MEC-assisted networks, due to both the presence of co-channel interference and the latency requirement of each offloading task, power control is tightly coupled with computation offloading, which becomes an obstacle to achieve the aim of energy saving. In this paper, we develop an analytic model to decouple power control and computation resource allocation from each other, in which the transmission power can be considered as a solution to a set of linear equations with a coefficient matrix depending on the computation resource budget. Based on this analytic foundation, we show that with a fixed offloading decision, the joint power control and computation resource allocation problem is invex, which ensures that every KKT (Karush-Kuhn-Tucker) stationary point of the problem must be a global minimizer. Moreover, we deduce a criterion for energy-efficient offloading decision making from the partial derivative of the total energy consumption of mobile devices with respect to the computation resource budget. Finally, we propose a heuristic algorithms to jointly optimizing power and computation resource allocation, and offloading decision. The numerical results demonstrate the optimality and efficiency of our proposed algorithm.