Multi-objective resource allocation for UAV-assisted air-ground integrated full-duplex OFDMA networks

Abstract

In multi-UAV-assisted air-ground integrated in-band full-duplex (IBFD) OFDMA networks, both uplink and downlink performances are critical and must be simultaneously considered. This study addresses effective resource allocation in such networks to maximize the total system uplink and downlink rates by jointly optimizing subcarrier assignment and power control. Given the significant trade-off between uplink and downlink transmissions owing to self-interference in IBFD systems and intercell interference, we formulate the resource allocation problem as a multi-objective optimization problem (MOOP), aiming to jointly maximize the uplink and downlink performances. To achieve Pareto optimal solutions, we employ the weighted Tchebycheff technique to transform the MOOP into a single-objective optimization problem (SOOP) and solve it using Successive Convex Approximation (SCA) within a Block Coordinate Descent (BCD) framework. This approach iteratively optimizes the subcarrier assignment and power control and effectively manages the trade-offs between uplink and downlink rates. The proposed method demonstrates the ability to achieve an efficient balance in resource allocation. Simulation results show that our method can obtain Pareto optimal solutions, demonstrating favorable performance trade-offs and fairness under various interference conditions, thereby improving the overall system performance in multi-UAV-assisted air-ground integrated OFDMA networks.