Joint Resources and Phase-Shift Optimization of MEC-Enabled UAV in IRS-Assisted 6G THz Networks

Abstract

Terahertz (THz) communication has the promise of enabling ultra-high data speeds in the sixth-generation (6G) wireless networks. Meanwhile, an intelligent reflecting surface (IRS) may influence incident electromagnetic wave propagation by changing the phase shifts with passive reflecting components. It can enhance spectrum efficiency and coverage capability, and minimize blockage vulnerability caused by severe THz wave propagation attenuation and poor diffraction. Recently, unmanned aerial vehicles (UAVs) have provided the services of aerial-based multi-access edge computing (MEC) ubiquitously. Motivated by above facts, this paper considers the IRS-assisted MEC-enabled UAV system for 6G THz communications networks. To that aim, the joint optimization of UAV computation power, IRS phase shift, and THz sub-band allocation are being explored to reduce total network latency. However, the designed problem is mixed-integer non-linear programming (MINLP), which is challenging to solve in polynomial time. Therefore, an iterative algorithm based on the Hungarian algorithm and the Whale-Optimization algorithm (WOA) is proposed to address this problem. The Hungarian algorithm optimizes the sub-band allocation while WOA optimizes the IRS phase shift. Finally, simulation results show that the proposed algorithm can reduce network latency by up to 50% compared to baseline algorithms.