# Non-iterative Optimization of Trajectory and Radio Resource for Aerial Network

arXiv: https://arxiv.org/abs/2405.01314 · HTML: https://mlsanity.com/paper/2405.01314/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 61 references that are in the map.
- First posted on arXiv 2 May 2024.

## Builds on (most cited first, 15 of 15)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [Joint Trajectory and Communication Design for Multi-UAV Enabled Wireless Networks](https://mlsanity.com/paper/1705.02723/) (2017)
- [A Vision and Framework for the High Altitude Platform Station (HAPS) Networks of the Future](https://mlsanity.com/paper/2007.15088/) (2020)
- [A Survey on Model-based, Heuristic, and Machine Learning Optimization Approaches in RIS-aided Wireless Networks](https://mlsanity.com/paper/2303.14320/) (2023)
- [Trajectory Design and Power Control for Multi-UAV Assisted Wireless Networks: A Machine Learning Approach](https://mlsanity.com/paper/1812.07665/) (2018)
- [UAV Trajectory Planning in Wireless Sensor Networks for Energy Consumption Minimization by Deep Reinforcement Learning](https://mlsanity.com/paper/2108.00354/) (2021)
- [Multi-UAV Interference Coordination via Joint Trajectory and Power Control](https://mlsanity.com/paper/1809.05697/) (2018)
- [RIS-Assisted UAV for Timely Data Collection in IoT Networks](https://mlsanity.com/paper/2103.17162/) (2021)
- [Joint Optimization of 3D Placement and Radio Resource Allocation for per-UAV Sum Rate Maximization](https://mlsanity.com/paper/2305.04519/) (2023)
- [Energy-Efficient Trajectory Design for UAV-Enabled Communication Under Malicious Jamming](https://mlsanity.com/paper/2009.06161/) (2020)
- [Trajectory Design for Distributed Estimation in UAV Enabled Wireless Sensor Network](https://mlsanity.com/paper/1805.04364/) (2018)
- [Trajectory Design and Power Allocation for Drone-Assisted NR-V2X Network with Dynamic NOMA/OMA](https://mlsanity.com/paper/2007.09097/) (2020)
- [Trajectory Optimization and Resource Allocation for OFDMA UAV Relay Networks](https://mlsanity.com/paper/2104.11091/) (2021)
- [Joint 3-D Positioning and Power Allocation for UAV Relay Aided by Geographic Information](https://mlsanity.com/paper/2110.05715/) (2021)

## Built on by (most cited first, 2 of 2)

- [LUCID: An Agentic AI Framework on Digital-Twin in the Loop for QoS-Guaranteeing Robotic Control](https://mlsanity.com/paper/2608.28437/) (2026)
- [Rate-Aware Quantum-Inspired Trajectory Learning for Interference-Limited Multi-UAV Networks](https://mlsanity.com/paper/2606.25480/) (2026)

## Abstract (arXiv)

We address a joint trajectory planning, user association, resource allocation, and power control problem to maximize proportional fairness in the aerial IoT network, considering practical end-to-end quality-of-service (QoS) and communication schedules. Though the problem is rather ancient, apart from the fact that the previous approaches have never considered user- and time-specific QoS, we point out a prevalent mistake in coordinate optimization approaches adopted by the majority of the literature. Coordinate optimization approaches, which repetitively optimize radio resources for a fixed trajectory and vice versa, generally converge to local optima when all variables are differentiable. However, these methods often stagnate at a non-stationary point, significantly degrading the network utility in mixed-integer problems such as joint trajectory and radio resource optimization. We detour this problem by converting the formulated problem into the Markov decision process (MDP). Exploiting the beneficial characteristics of the MDP, we design a non-iterative framework that cooperatively optimizes trajectory and radio resources without initial trajectory choice. The proposed framework can incorporate various trajectory-planning algorithms such as the genetic algorithm, tree search, and reinforcement learning. Extensive comparisons with diverse baselines verify that the proposed framework significantly outperforms the state-of-the-art method, nearly achieving the global optimum. Our implementation code is available at https://github.com/hslyu/dbspf.{https://github.com/hslyu/dbspf}.