# Robust Energy-Efficient DRL-Based Optimization in UAV-Mounted RIS Systems with Jitter

arXiv: https://arxiv.org/abs/2506.17971 · HTML: https://mlsanity.com/paper/2506.17971/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jun 2025.

## Built on by (most cited first, 1 of 1)

- [Decision Transformer for UAV-Mounted RIS-Assisted Dynamic D2D Communications](https://mlsanity.com/paper/2609.09885/) (2026)

## Abstract (arXiv)

In this letter, we propose an energy-efficient design for an unmanned aerial vehicle (UAV)-mounted reconfigurable intelligent surface (RIS) communication system with nonlinear energy harvesting (EH) and UAV jitter. A joint optimization problem is formulated to maximize the EH efficiency of the UAV-mounted RIS by controlling the user powers, RIS phase shifts, and time-switching factor, subject to quality of service and practical EH constraints. The problem is nonconvex and time-coupled due to UAV angular jitter and nonlinear EH dynamics, making it intractable for conventional optimization methods. To address this, we reformulate the problem as a deep reinforcement learning (DRL) environment and develop a smoothed softmax dual deep deterministic policy gradient algorithm. The proposed method incorporates action clipping, entropy regularization, and softmax-weighted Q-value estimation to improve learning stability and exploration. Simulation results show that the proposed algorithm converges reliably under various UAV jitter levels and achieves an average EH efficiency of 45.07%, approaching the 53.09% upper bound of exhaustive search, and outperforming other DRL baselines.