🤖 AI Summary
To address the challenges of ubiquitous connectivity, resilient coverage, and intelligent services in highly dynamic 6G environments, this paper proposes a distributed wireless architecture based on Stacked Intelligent Reflecting Surfaces (SIRS). The architecture introduces a multi-layer electromagnetic control mechanism to overcome the limited degrees of freedom inherent in single-layer reconfigurable surfaces, and establishes a collaborative multi-layer control framework integrating hierarchical signal processing, AI-driven user association optimization, and joint precoding. This enables adaptive beamforming and secure transmission under dynamic channel conditions. Experimental results demonstrate significant improvements in spectral and energy efficiency, enhanced physical-layer security, and superior performance in interference suppression and network robustness. The proposed SIRS-based architecture thus establishes a novel paradigm for realizing intelligent wireless environments in 6G systems.
📝 Abstract
The sixth-generation (6G) wireless networks are expected to deliver ubiquitous connectivity, resilient coverage, and intelligence-driven services in highly dynamic environments. To achieve these goals, distributed wireless architectures such as cell-free massive multiple-input multiple-output (MIMO) have attracted significant attention due to their scalability and fairness. Recently, stacked intelligent metasurfaces (SIMs) have emerged as a promising evolution of reconfigurable intelligent surfaces, offering multi-layer electromagnetic domain processing with enhanced controllability and spatial degrees of freedom. By integrating SIMs into distributed wireless networks, advanced wave-domain operations can be realized, enabling efficient interference management, improved energy and spectral efficiency, and robust physical-layer security. This article provides a comprehensive overview of SIM-aided distributed wireless networks, including their application scenarios, classification, and system architectures. Key signal processing challenges, such as hierarchical frameworks, user association, and joint precoding, are discussed, followed by case studies demonstrating significant performance gains. Finally, future research directions in hardware design, energy consumption modeling, algorithm development, and artificial intelligence integration are highlighted, aiming to pave the way for scalable and intelligent 6G distributed wireless networks.