Mobile edge computing (MEC) has been envisaged as a promising technique in fifth generation (5G) and beyond wireless networks. In order to alleviate the explosive growth of computation and spectrum demand, cognitive radio (CR) and unmanned aerial vehicles (UAV) are studied in MEC-aware networks. In this study, considering a local computation and partial offloading scheme, a UAV-enabled CR-MEC framework is proposed and the authors' aim is to maximise the energy efficiency (EE) of the wireless devices (WDs). The formulated optimisation problem is not convex and challenging to be solved. To deal with it, an equivalent reformulation of this EE maximisation problem is introduced, and the authors decompose the original problem into two sub-problems, wherein the sub-problems become tractable and can be solved by jointly optimising sensing time, offloading power and WD-UAV scheduling. Numerical results highlight the EE enhancement with various system parameters and reveal the superiority of the proposed algorithm than other schemes with low computational complexity.

中文翻译：

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认知无线电网络中支持无人机的移动边缘计算的高效设计优化

移动边缘计算（MEC）已被设想为第五代（5G）以及无线网络之外的一种有前途的技术。为了缓解计算和频谱需求的爆炸性增长，在支持MEC的网络中研究了认知无线电（CR）和无人机（UAV）。在这项研究中，考虑到本地计算和部分卸载方案，提出了一种支持无人机的CR-MEC框架，作者的目标是使无线设备（WD）的能效（EE）最大化。所提出的优化问题不是凸面的，并且有待解决。为了解决这个问题，引入了等效的EE最大化问题的公式化，作者将原始问题分解为两个子问题，这些子问题变得易于处理，可以通过共同优化感测时间来解决，卸载功率和WD-UAV调度。数值结果突出了在各种系统参数下的EE增强，并显示了该算法比其他计算复杂度低的方案的优越性。