Problem
Research questions and friction points this paper is trying to address.
Reconfigurable Intelligent Surfaces
Millimeter Wave Communications
Phase Masks
Embedded Microcontroller
Adaptability
Adaptive RIS-aided Communications through ML-based Generation of Phase Masks
🤖 AI Summary
本文针对RIS在毫米波通信中因存储限制而难以适应变化信道条件的问题，提出了一种基于机器学习动态生成相位掩码的方法。
📝 Abstract
Reconfigurable Intelligent Surfaces (RISs) are an attractive technology for Millimeter Wave (mmWave) communications due to their ability to passively reflect incident signals. However, current implementations of RIS rely on performing computationally-intensive algorithms offline to generate phase masks, which are stored as a codebook on the embedded microcontroller on the RIS. The codebook size is restricted by the embedded microcontroller's storage capacity, which limits the ability of the RIS to adapt to evolving channel conditions and deployment scenarios. In this demo, we showcase an Machine Learning (ML)-based solution for dynamically generating new phase masks during runtime. Our approach leverages a ML model deployed on the microcontroller for approximating the output of a phase mask generation algorithm, responding to new inputs while remaining smaller than a codebook.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Machine Learning
Phase Masks
Reconfigurable Intelligent Surfaces
Dynamic Generation
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