Multi-Objective Resource Allocation for UAV-Assisted Air-Ground Integrated MC-NOMA Networks - Wang, Tong (IEEE access, 2024)
We consider a Multi-UAV multicarrier non-orthogonal multiple access (MC-NOMA) downlink network in which each UAV serves a group of ground users within its designated cell. Our goal is to maximize the total downlink rate for each cell by simultaneously optimizing the subcarrier assignment and power control. However, the need to maximize the downlink rate for each individual UAV cell introduces conflicts because the optimization objectives for different cells are inherently at odds with each other, making this a Multi-Objective Optimization Problem (MOOP). We applied the weighted Tchebycheff method to convert the MOOP into a Single-Objective Optimization Problem (SOOP). The resulting SOOP remains a Mixed-Integer Nonlinear Programming (MINLP) problem. To address this, we first relax the combinatorial subcarrier assignment variables into continuous variables and then apply a penalty method to enforce binary constraints. To handle the nonconvexity of the objective function and constraints, we utilize the Big-M method and Successive Convex Approximation (SCA) to decouple the product terms and deal with nonconvexity. We then employed an iterative approach to obtain a suboptimal solution and continued the process until convergence was achieved. Simulations demonstrate that this method effectively balances the trade-offs among different cells, achieves significant performance improvements and successfully approximates Pareto optimal solutions.