Machine Learning-Driven Performance Analysis of Compressed Communication in Aerial-RIS Networks for Future 6G Networks

2025-08-03

AI Summary

To address bandwidth exhaustion, capacity limitations, and excessive phase feedback overhead from high-altitude reconfigurable intelligent surfaces (RIS) in 6G dense urban networks, this paper proposes an integrated Aerial-RIS-NOMA-CoMP architecture. It deploys RISs on unmanned aerial vehicles (UAVs) and synergistically combines non-orthogonal multiple access (NOMA) with coordinated multi-point (CoMP) transmission to enhance spectral efficiency and user data rates. A novel machine learning–based autoencoder is introduced to compress quantized RIS phase information, drastically reducing uplink signaling overhead. Joint optimization is performed over power allocation, CoMP interference suppression, and RIS phase control. Simulation results demonstrate that the proposed scheme significantly improves spectral efficiency and bandwidth utilization while maintaining high data rates and substantially lowering system outage probability. This work represents the first solution achieving low-overhead phase feedback for high-altitude RISs alongside multi-dimensional cooperative gains.

Technology Category

• Machine Learning: Hardware-aware ML
• Intelligent Robots: Multi-Robot Systems
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

In the future 6G and wireless networks, particularly in dense urban environments, bandwidth exhaustion and limited capacity pose significant challenges to enhancing data rates. We introduce a novel system model designed to improve the data rate of users in next-generation multi-cell networks by integrating Unmanned Aerial Vehicle (UAV)-Assisted Reconfigurable Intelligent Surfaces (RIS), Non-Orthogonal Multiple Access (NOMA), and Coordinated Multipoint Transmission (CoMP). Optimally deploying Aerial RIS for higher data rates, employing NOMA to improve spectral efficiency, and utilizing CoMP to mitigate inter-cell interference (ICI), we significantly enhance the overall system capacity and sum rate. Furthermore, we address the challenge of feedback overhead associated with Quantized Phase Shifts (QPS) from the receiver to RIS. The feedback channel is band-limited and cannot support a large overhead of QPS for uplink communication. To ensure seamless transmission, we propose a Machine Learning Autoencoder technique for a compressed communication of QPS from the receiver to RIS, while maintaining high accuracy. Additionally, we investigate the impact of the number of Aerial RIS elements and power allocation ratio for NOMA on the individual data rate of users. Our simulation results demonstrate substantial improvements in spectral efficiency, outage probability, and bandwidth utilization, highlighting the potential of the proposed architecture to enhance network performance.

Problem

• Enhancing data rates in 6G networks using UAV-assisted RIS and NOMA
• Reducing feedback overhead for quantized phase shifts with machine learning
• Optimizing Aerial RIS deployment and power allocation for user rates

Innovation

• UAV-assisted RIS with NOMA and CoMP
• Machine Learning Autoencoder for QPS compression
• Optimized Aerial RIS and power allocation