Energy Minimization of Inland Waterway USVs for IRS-Assisted Hybrid UAV-Terrestrial MEC Network.

Due to its exceptional ability to create favorable line-of-sight (LoS) propagation environments, the intelligent reflecting surface (IRS) is widely recognized as a technological enabler for wireless inland waterway communications. In this paper, an IRS-assisted hybrid unmanned aerial vehicles (UAV)-terrestrial network architecture with unmanned surface vehicles (USVs) is proposed and a USVs energy minimization problem is formulated by jointly considering offloading decisions, computation capability, beamforming vector design and IRS phase shift-vector. To address the formulated problem, we decouple the original problem into two subproblems, in which the first subproblem focuses on joint offloading decisions and computation capability and the second subproblem concerns joint IRS phase shift-vector and the beamforming vector design. The enhanced differential evolution algorithm (EDE) is proposed to solve the former subproblem, and the minimum-variance-distortionless-response (MVDR) and enhanced min-maximization (EMM) algorithms are proposed to obtain optimized beamforming vector and IRS phase shift-vector in the second subproblem, respectively. Simulation results show how the proposed solution realizes a good tradeoff between network energy consumption and capacity in comparison to three alternative algorithms. The results also show that the proposed algorithm can improve the network performance in terms of the number of successfully offloaded tasks.