Stacked Intelligent Metasurfaces for Next-Generation Wireless Signal Processing: A Comprehensive Survey

Abstract

Confronted with the stringent demands of the sixth-generation (6G) wireless networks, such as ultra-high data rates, minimal latency, and high energy efficiency, conventional fully-digital massive multiple-input multiple-output (MIMO) systems face significant challenges in scalability, hardware complexity, and computational overhead. To address these challenges, stacked intelligent metasurfaces (SIMs) emerge as a promising technology that leverages multi-layer programmable meta-atoms to perform complex signal processing tasks directly in the wave domain at the speed of light. This paper provides a comprehensive survey of SIM, beginning with its physical principles and hardware design. We then review novel SIM-enabled signal processing frameworks and summarize their reported performance gains across representative applications, including holographic MIMO, direction-of-arrival estimation, multi-user beamforming, satellite communications, as well as integrated sensing and communications. We also discuss the integration of SIM with machine learning methods, including reinforcement learning and semantic communications, highlighting the advantages of low latency and high energy efficiency inherent to wave-based computation. Additionally, we identify key practical challenges including hardware imperfections, inter-layer coupling effects, high-dimensional optimization complexity, and system-level integration, while proposing viable solutions such as model calibration, robust training frameworks, and optimization strategies powered by artificial intelligence. Emerging evidence suggests that SIM technology possesses significant potential to reduce reliance on conventional power-intensive digital baseband processing modules, thereby establishing a scalable, sustainable, and intelligent infrastructure for integrated sensing, communication, and computing networks in the 6G era.