IRS-Assisted AAV-MEC-NOMA Networks: A Joint Optimization Design for Computational Capacity

To cope with the challenges of traditional mobile edge computing (MEC), such as inflexibility and limited coverage, unmanned aerial vehicle (UAV)-MEC has been proposed as an important paradigm. However, the UAV-MEC systems are still facing the severe channel fading and obstacle blocking, which are fortunately expected to be solved by the emerging intelligent reflective surface (IRS) technique. In this paper, we propose an IRS-assisted UAV-MEC framework, where a UAV employs nonorthogonal multiple access (NOMA) scheme to offload partial computation tasks to multiple ground MEC servers connecting to access points (APs) with the assistance of IRS. The problem of maximizing computational capacity of the network is proposed by jointly optimizing the phase shift of IRS, transmit power and computation resource allocation of UAV, NOMA decoding order, and UAV trajectory. Due to the complex non-convexity of the problem, we decompose the original problem into three subproblems based on the alternating optimization (AO) structure. To be specific, we first derive the IRS reflection phase shift by using the semidefinite relaxation (SDR) and concave-convex procedure (CCCP) methods. Then, we obtain the computation and communication resource allocation by using the CCCP method. Finally, we determine the optimal UAV trajectory and decoding order by using the successive convex approximation (SCA) approach and the penalty-based method. A joint optimization algorithm is proposed for the UAV-MEC-NOMA framework. Numerical results demonstrate that: 1) The proposed algorithm is efficient and superior to other benchmark schemes in terms of increasing computational capacity; 2) The application of IRS significantly enhances the computation capability of the network; 3) The NOMA scheme outperforms the orthogonal multiple access (OMA) scheme.