AI & ML
Optimization of Joint Beamforming in RIS-aided Multi-carrier and Multi-user MIMO Systems
arXiv cs.NI0 views
arXiv:2611.05796v1 Announce Type: new Abstract: Reconfigurable intelligent surface (RIS)-aided multiple-input multiple-output (MIMO) communication is a promising enabler for beyond-fifth-generation wireless networks. However, RIS phase shifters typically rely on a tuning element (for example a variable-length stub), which can be optimally configured for only one carrier frequency at a time. This constraint complicates RIS deployment in multi-carrier systems and often necessitates additional measures, such as using multiple RIS panels or partitioning RIS elements across carriers. In this work, we propose a novel scheme in which all RIS elements simultaneously serve multiple carriers, delivering improved performance over existing approaches. Our method leverages the multiple reflections captured by physically consistent RIS models to realize the desired phase shifts for all carriers using a single stub length setting. We further develop a joint active and passive beamforming optimization framework tailored to multi-carrier, physically consistent RIS-aided systems. Simulation results confirm the effectiveness of the proposed
Source:https://arxiv.org/abs/2611.05796PreviousEnergy-efficient End-to-End Communication System with Spiking Neural NetworksNextStarWorld: A Cross-Layer World Model for Starlink Prediction and Streaming
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