Energy-Efficient Designs for SIM-Based Broadcast MIMO Systems

2024-09-01

arXiv.org

AI Summary

This paper addresses the energy efficiency (EE) maximization problem in broadcast multiple-input multiple-output (MIMO) systems enabled by stacked intelligent metasurfaces (SIMs). To jointly optimize the transmit covariance matrix and the multi-layer phase responses of the SIM, we propose a novel hybrid framework integrating BC-MAC duality with successive convex approximation (SCA) and the Dinkelbach algorithm, combined with projected gradient descent and linear precoding—replacing computationally intensive dirty-paper coding to reduce complexity. Theoretically, we characterize the coupled impact of SIM stack depth and unit distribution on both EE and sum rate. Simulation results demonstrate that the proposed scheme significantly improves EE under practical hardware constraints, while the number of SIM units and inter-layer configuration critically govern system performance. This work establishes a new design paradigm for efficient, implementation-ready SIM-aided broadcast MIMO systems.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Machine Learning: Efficient ML / Green AI
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

Stacked intelligent metasurface (SIM), which consists of multiple layers of intelligent metasurfaces, is emerging as a promising solution for future wireless communication systems. In this timely context, we focus on broadcast multiple-input multiple-output (MIMO) systems and aim to characterize their energy efficiency (EE) performance. To gain a comprehensive understanding of the potential of SIM, we consider both dirty paper coding (DPC) and linear precoding and formulate the corresponding EE maximization problems. For DPC, we employ the broadcast channel (BC)-multiple-access channel (MAC) duality to obtain an equivalent problem, and optimize users' covariance matrices using the successive convex approximation (SCA) method, which is based on a tight lower bound of the achievable sum-rate, in combination with Dinkelbach's method. Since optimizing the phase shifts of the SIM meta-elements is an optimization problem of extremely large size, we adopt a conventional projected gradient-based method for its simplicity. A similar approach is derived for the case of linear precoding. Simulation results show that the proposed optimization methods for the considered SIM-based systems can significantly improve the EE, compared to the conventional counterparts. Also, we demonstrate that the number of SIM meta-elements and their distribution across the SIM layers have a significant impact on both the achievable sum-rate and EE performance.

Problem

• Maximizing energy efficiency in SIM-based broadcast MIMO systems
• Optimizing phase shifts for large-scale metasurface elements
• Comparing dirty paper coding and linear precoding performance impacts

Innovation

• Stacked intelligent metasurface enables energy-efficient MIMO
• Successive convex approximation optimizes user covariance matrices
• Projected gradient method simplifies phase shift optimization