This is the Simulation code for MEC enabled UAV with IRS assisted 6G THz Networks
Terrahertz (THz) communication is likely to enable ultra-fast data speeds in sixth-generation (6G) wireless networks. On the other hand, intelligent reflective surfaces (IRSs) can affect incident electromagnetic propagation by changing phase shifts to manual reflective components. It is possible to improve spectral efficiency and coverage capabilities, and minimize cut-off vulnerabilities due to severe THz propagation attenuation and poor diffraction. Recently, unmanned aerial vehicles (UAVs) have provided services of airborne-based multi-access edge computing (MECs) in a ubiquitous manner. Motivated by the above facts, this paper considers an IRS-enabled MEC-enabled UAV system for 6GHz communication networks. To this end, we explore a joint optimization of UAV computational capability, IRS phase shift, and THz subband allocation to reduce total network latency. However, the designed problem is difficult to solve in polynomial time with mixed integer nonlinear programming (MINLP). Thus, to solve this problem, an iterative algorithm based on the Hungarian algorithm and the Whale Optimization Algorithm (WOA) is proposed. The Hungarian algorithm optimizes the subband allocation while the WOA optimizes the IRS phase shift.
- Unity 2020.3.25f1
- ML-agents Release 17
- python 3.8
- Pytorch 1.8.0
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