Efficient design optimisation for UAV-enabled mobile edge computing in cognitive radio networks

Abstract

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.