Technology Category
Search and Optimization: Mixed Discrete/Continuous SearchPlanning, Routing, and Scheduling: Optimization of Spatio-temporal SystemsConstraint Satisfaction and Optimization: Distributed CSP/Optimization
Cell-Free Massive MIMO SWIPT with Beyond Diagonal Reconfigurable Intelligent Surfaces
🤖 AI Summary
This work addresses the simultaneous wireless information and power transfer (SWIPT) challenge in cell-free massive MIMO (CF-mMIMO) systems. We propose a joint optimization framework integrating a block-diagonal reconfigurable intelligent surface (BDRIS), enabling coexistence of information receivers (IRs) and energy receivers (ERs). To overcome practical CSI acquisition constraints, we design a protective partial zero-forcing precoder leveraging only long-term statistical channel state information (CSI), and jointly optimize access point selection, power allocation, and the BDRIS scattering matrix. Unlike conventional diagonal RISs, our BDRIS decouples phase control across blocks, eliminating restrictive phase coupling. Efficient solution is achieved via heuristic search, successive convex approximation, and deep reinforcement learning. Simulation results demonstrate that, while guaranteeing target communication rates, the proposed scheme boosts average harvested energy at ERs by up to 7× compared to diagonal-RIS-based SWIPT, significantly enhancing SWIPT energy efficiency and system flexibility.
Technology Category
Application Category
📝 Abstract
We investigate the integration of beyond diagonal reconfigurable intelligent surfaces (BDRISs) into cell free massive multiple input multiple output (CFmMIMO) systems to enhance simultaneous wireless information and power transfer (SWIPT). To simultaneously support two groups of users energy receivers (ERs) and information receivers (IRs) without sacrificing time frequency resources, a subset of access points (APs) is dedicated to serving ERs with the aid of a BDRIS, while the remaining APs focus on supporting IRs. A protective partial zero forcing precoding technique is implemented at the APs to manage the non coherent interference between the ERs and IRs. Subsequently, closed form expressions for the spectral efficiency of the IRs and the average sum of harvested energy at the ERs are leveraged to formulate a comprehensive optimization problem. This problem jointly optimizes the AP selection, AP power control, and scattering matrix design at the BDRIS, all based on long term statistical channel state information. This challenging problem is then effectively transformed into more tractable forms. To solve these sub problems, efficient algorithms are proposed, including a heuristic search for the scattering matrix design, as well as successive convex approximation and deep reinforcement learning methods for the joint AP mode selection and power control design. Numerical results show that a BDRIS with a group or fully connected architecture achieves significant energy harvesting gains over the conventional diagonal RIS, especially delivering up to a seven fold increase in the average sum of harvested energy when a heuristic based scattering matrix design is employed.
Problem
Research questions and friction points this paper is trying to address.
Enhance SWIPT in CFmMIMO using BDRIS for energy and information transfer
Optimize AP selection, power control, and BDRIS design jointly
Improve energy harvesting with BDRIS over conventional RIS architectures
Innovation
Methods, ideas, or system contributions that make the work stand out.
BDRIS enhances SWIPT in CFmMIMO systems
Protective partial zero forcing precoding manages interference
Heuristic and deep learning optimize AP and BDRIS
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