Cell-Free Massive MIMO-Assisted SWIPT Using Stacked Intelligent Metasurfaces

2026-02-24

AI Summary

This study addresses the joint optimization of access point operating modes, reconfigurable intelligent surface (RIS) phase shifts, and power allocation in a cell-free massive MIMO-SWIPT system enhanced by stacked RISs, under practical constraints including nonlinear energy harvesting, pilot contamination, channel estimation errors, and reliance solely on long-term statistical channel state information. The work aims to simultaneously maximize harvested energy while meeting spectral efficiency requirements. For the first time, stacked RISs are introduced into this context, and a centralized training–decentralized execution (CTDE) deep reinforcement learning framework is proposed. By modeling the environment as a Markov decision process and employing a normalized joint reward function, the method efficiently tackles the high-dimensional mixed-integer nonconvex optimization problem. Experimental results demonstrate that the proposed approach significantly outperforms conventional schemes under dual constraints and achieves performance close to the convex optimization theoretical upper bound, confirming its effectiveness and robustness.

Technology Category

Search and Optimization: Learning to SearchIntelligent Robots: Learning & Optimization for ROBPlanning, 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 environmentsSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingSecurity and Privacy: Large-scale security measurements

Abstract

This study explores a next-generation multiple access (NGMA) framework for cell-free massive MIMO (CF-mMIMO) systems enhanced by stacked intelligent metasurfaces (SIMs), aiming to improve simultaneous wireless information and power transfer (SWIPT) performance. A fundamental challenge lies in optimally selecting the operating modes of access points (APs) to jointly maximize the received energy and satisfy spectral efficiency (SE) quality-of-service constraints. Practical system impairments, including a non-linear harvested energy model, pilot contamination (PC), channel estimation errors, and reliance on long-term statistical channel state information (CSI), are considered. We derive closed-form expressions for both the achievable SE and the average sum harvested energy (sum-HE). A mixed-integer non-convex optimization problem is formulated to jointly optimize the SIM phase shifts, APs mode selection, and power allocation to maximize average sum-HE under SE and average harvested energy constraints. To solve this problem, we propose a centralized training, decentralized execution (CTDE) framework based on deep reinforcement learning (DRL), which efficiently handles high-dimensional decision spaces. A Markovian environment and a normalized joint reward function are introduced to enhance the training stability across on-policy and off-policy DRL algorithms. Additionally, we provide a two-phase convex-based solution as a theoretical robust performance. Numerical results demonstrate that the proposed DRL-based CTDE framework achieves SWIPT performance comparable to convexification-based solution, while significantly outperforming baselines.

Problem

• Cell-Free Massive MIMO
• SWIPT
• Intelligent Metasurfaces
• Access Point Mode Selection
• Spectral Efficiency

Innovation

• Cell-Free Massive MIMO
• Stacked Intelligent Metasurfaces
• SWIPT
• Deep Reinforcement Learning
• Mixed-Integer Non-Convex Optimization