# Resource Scheduling for UAVs-aided D2D Networks: A Multi-objective Optimization Approach

arXiv: https://arxiv.org/abs/2311.16116 · HTML: https://mlsanity.com/paper/2311.16116/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Sep 2023.

## Built on by (most cited first, 4 of 4)

- [Graph Attention-based Reinforcement Learning for Trajectory Design and Resource Assignment in Multi-UAV Assisted Communication](https://mlsanity.com/paper/2401.17880/) (2024)
- [Bridging Earth and Space: A Survey on HAPS for Non-Terrestrial Networks](https://mlsanity.com/paper/2510.19731/) (2025)
- [Joint Link Adaptation and Device Scheduling Approach for URLLC Industrial IoT Network: A DRL-based Method with Bayesian Optimization](https://mlsanity.com/paper/2512.23493/) (2025)
- [RIS-empowered Topology Control for Distributed Learning in Urban Air Mobility](https://mlsanity.com/paper/2403.05133/) (2024)

## Abstract (arXiv)

Unmanned aerial vehicles (UAVs)-aided device-todevice (D2D) networks have attracted great interests with the development of 5G/6G communications, while there are several challenges about resource scheduling in UAVs-aided D2D networks. In this work, we formulate a UAVs-aided D2D network resource scheduling optimization problem (NetResSOP) to comprehensively consider the number of deployed UAVs, UAV positions, UAV transmission powers, UAV flight velocities, communication channels, and UAV-device pair assignment so as to maximize the D2D network capacity, minimize the number of deployed UAVs, and minimize the average energy consumption over all UAVs simultaneously. The formulated NetResSOP is a mixed-integer programming problem (MIPP) and an NP-hard problem, which means that it is difficult to be solved in polynomial time. Moreover, there are trade-offs between the optimization objectives, and hence it is also difficult to find an optimal solution that can simultaneously make all objectives be optimal. Thus, we propose a non-dominated sorting genetic algorithm-III with a Flexible solution dimension mechanism, a Discrete part generation mechanism, and a UAV number adjustment mechanism (NSGA-III-FDU) for solving the problem comprehensively. Simulation results demonstrate the effectiveness and the stability of the proposed NSGA-III-FDU under different scales and settings of the D2D networks.