This repository contains the code and workings that I undertook as part of my MEngSc Thesis, titled "Quantum-Assisted Optimisation of Secret Key Rate Exchange for UAV Communications using a Rician Channel Model for Physical Layer Security".
The work outlined in this repository is the simulation of a Quantum Deep Reinforcement Learning (QDRL) algorithm for the optimisation of secret key rate exchange between a UAV base station (UAV-BS) and a set of legitimate, authenticated ground users that are subject to eavesdropping.
The thesis report for this project can be found at https://github.com/piersk/MEngSc_Thesis_Report.
This problem has been modelled as a joint optimisation problem with a set of subproblems. The objective of the joint optimisation problem is to maximise the secrecy rate of the UAV-LU communications, subject to a set of constraints.
- Maximisation of Secrecy Rate
- Maximisation of Data Exchange Rate
- Maximisation of Energy Efficiency
- Minimisation of Energy Consumption
- Minimisation of UAV Trajectory to Optimal Location in 3-D Cartesian Space
The proposed and outlined method, using the programs in this repository under final_submission_code/ exhibited convergence towards the optimal values for the joint optimisation problem and all of its subproblems.