Multi-Objective Resource Allocation for UAV-Assisted Air-Ground Integrated MC-NOMA Networks

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 c...

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Bibliographic Details
Published in:IEEE access Vol. 12; pp. 141000 - 141012
Main Author: Wang, Tong
Format: Journal Article
Language:English
Published: Piscataway IEEE 2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Autonomous aerial vehicles
big-m method
Combinatorial analysis
Constraints
Continuity (mathematics)
Downlink
Downlinking
Mixed integer
Mixed integer linear programming
mixed integer nonlinear problem (MINLP)
multi-objective optimization problem (MOOP)
Multiple objective analysis
NOMA
Non-orthogonal multiple access (NOMA)
Nonlinear programming
Nonorthogonal multiple access
Optimization
Pareto optimization
Pareto optimum
Power control
Programming
Resource allocation
Resource management
subcarrier assignment
Subcarriers
successive convex approximation (SCA)
ISSN:2169-3536, 2169-3536
Online Access:Get full text
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Summary: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.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3467069