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Performance Comparison of Aerial RIS and STAR-RIS in 3D Wireless Environments

Dongdong Yang, Bin Li, Jiguang He

TL;DR

This work compares aerial RIS and STAR-RIS architectures in 3D wireless environments by developing accurate 3D directional-channel models and a WMMSE-BCD-PDD optimization framework to maximize sum rate. It demonstrates that STAR-RIS provides superior performance at low altitudes due to full-space coverage, while conventional RIS performs better near the base station at higher altitudes. The results yield practical deployment insights and highlight how orientation and altitude critically shape performance in UAV-based intelligent surfaces for future 6G networks.

Abstract

Reconfigurable intelligent surface (RIS) and simultaneously transmitting and reflecting RIS (STAR-RIS) have emerged as key enablers for enhancing wireless coverage and capacity in next-generation networks. When mounted on unmanned aerial vehicles (UAVs), they benefit from flexible deployment and improved line-of-sight conditions. Despite their promising potential, a comprehensive performance comparison between aerial RIS and STAR-RIS architectures has not been thoroughly investigated. This letter presents a detailed performance comparison between aerial RIS and STAR-RIS in three-dimensional wireless environments. Accurate channel models incorporating directional radiation patterns are established, and the influence of deployment altitude and orientation is thoroughly examined. To optimize the system sum-rate, we formulate joint optimization problems for both architectures and propose an efficient solution based on the weighted minimum mean square error and block coordinate descent algorithms. Simulation results reveal that STAR-RIS outperforms RIS in low-altitude scenarios due to its full-space coverage capability, whereas RIS delivers better performance near the base station at higher altitudes. The findings provide practical insights for the deployment of aerial intelligent surfaces in future 6G communication systems.

Performance Comparison of Aerial RIS and STAR-RIS in 3D Wireless Environments

TL;DR

This work compares aerial RIS and STAR-RIS architectures in 3D wireless environments by developing accurate 3D directional-channel models and a WMMSE-BCD-PDD optimization framework to maximize sum rate. It demonstrates that STAR-RIS provides superior performance at low altitudes due to full-space coverage, while conventional RIS performs better near the base station at higher altitudes. The results yield practical deployment insights and highlight how orientation and altitude critically shape performance in UAV-based intelligent surfaces for future 6G networks.

Abstract

Reconfigurable intelligent surface (RIS) and simultaneously transmitting and reflecting RIS (STAR-RIS) have emerged as key enablers for enhancing wireless coverage and capacity in next-generation networks. When mounted on unmanned aerial vehicles (UAVs), they benefit from flexible deployment and improved line-of-sight conditions. Despite their promising potential, a comprehensive performance comparison between aerial RIS and STAR-RIS architectures has not been thoroughly investigated. This letter presents a detailed performance comparison between aerial RIS and STAR-RIS in three-dimensional wireless environments. Accurate channel models incorporating directional radiation patterns are established, and the influence of deployment altitude and orientation is thoroughly examined. To optimize the system sum-rate, we formulate joint optimization problems for both architectures and propose an efficient solution based on the weighted minimum mean square error and block coordinate descent algorithms. Simulation results reveal that STAR-RIS outperforms RIS in low-altitude scenarios due to its full-space coverage capability, whereas RIS delivers better performance near the base station at higher altitudes. The findings provide practical insights for the deployment of aerial intelligent surfaces in future 6G communication systems.

Paper Structure

This paper contains 10 sections, 28 equations, 4 figures.

Table of Contents

  1. Introduction
  2. System model
  3. Channel Model
  4. Signal Model
  5. Problem Formulation
  6. Update $\{ {\bm \varpi} ,{\bm\nu}\}$
  7. Update ${\bf W}$ and $\{ {\bm \theta _t},{\bm \theta _r}\}$
  8. Update $\{\tilde{\boldsymbol{\beta}}_{t},\tilde{\boldsymbol{\beta}}_{r}\}$ and $\{\tilde{\boldsymbol{\phi}}_{t},\tilde{\boldsymbol{\phi}}_{r}\}$
  9. simulation results
  10. conclusion

Figures (4)

  • Figure 1: Aerial RIS and STAR-RIS architectures.
  • Figure 2: The convergence performance of the proposed algorithm.
  • Figure 3: Effect of various deployment positions and altitudes on the sum rate of the RIS and STAR-RIS architectures.
  • Figure 4: Effect of altitude and orientation on the sum rate of the RIS and STAR-RIS architectures.