Joint Optimization of UAV-Carried IRS for Urban Low Altitude mmWave Communications with Deep Reinforcement Learning

2025-01-06

AI Summary

In urban low-altitude millimeter-wave (mmWave) communications, line-of-sight (LoS) links suffer severe signal attenuation, low data rates, and poor energy efficiency due to building blockage and multipath interference. To address this, this paper proposes an intelligent reflecting surface (IRS)-assisted UAV relaying architecture, jointly optimizing IRS phase shifts and the UAV’s three-dimensional trajectory. We devise a novel end-to-end deep reinforcement learning framework that integrates neural episodic control (NEC) with long short-term memory (LSTM) networks to efficiently solve the non-convex joint optimization problem. This design significantly improves training stability and convergence speed over conventional methods. Simulation results demonstrate that the proposed scheme outperforms benchmark algorithms in average achievable rate, energy efficiency, and link reliability—effectively mitigating mmWave propagation bottlenecks in dense urban environments.

Technology Category

• Intelligent Robots: Learning & Optimization for ROB
• Search and Optimization: Learning to Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

Emerging technologies in sixth generation (6G) of wireless communications, such as terahertz communication and ultra-massive multiple-input multiple-output, present promising prospects. Despite the high data rate potential of millimeter wave communications, millimeter wave (mmWave) communications in urban low altitude economy (LAE) environments are constrained by challenges such as signal attenuation and multipath interference. Specially, in urban environments, mmWave communication experiences significant attenuation due to buildings, owing to its short wavelength, which necessitates developing innovative approaches to improve the robustness of such communications in LAE networking. In this paper, we explore the use of an unmanned aerial vehicle (UAV)-carried intelligent reflecting surface (IRS) to support low altitude mmWave communication. Specifically, we consider a typical urban low altitude communication scenario where a UAV-carried IRS establishes a line-of-sight (LoS) channel between the mobile users and a source user (SU) despite the presence of obstacles. Subsequently, we formulate an optimization problem aimed at maximizing the transmission rates and minimizing the energy consumption of the UAV by jointly optimizing phase shifts of the IRS and UAV trajectory. Given the non-convex nature of the problem and its high dynamics, we propose a deep reinforcement learning-based approach incorporating neural episodic control, long short-term memory, and an IRS phase shift control method to enhance the stability and accelerate the convergence. Simulation results show that the proposed algorithm effectively resolves the problem and surpasses other benchmark algorithms in various performances.

Problem

• Millimeter Wave Communication
• Signal Degradation
• Urban Environment

Innovation

• Deep Learning
• Smart Reflecting Surfaces
• 6G Wireless Communication