A Deep Reinforcement Learning Approach for Autonomous Reconfigurable Intelligent Surfaces Article Swipe

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Hyuckjin Choi , Ly V. Nguyen , Junil Choi , A. Lee Swindlehurst ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2403.09270

A reconfigurable intelligent surface (RIS) is a prospective wireless technology that enhances wireless channel quality. An RIS is often equipped with passive array of elements and provides cost and power-efficient solutions for coverage extension of wireless communication systems. Without any radio frequency (RF) chains or computing resources, however, the RIS requires control information to be sent to it from an external unit, e.g., a base station (BS). The control information can be delivered by wired or wireless channels, and the BS must be aware of the RIS and the RIS-related channel conditions in order to effectively configure its behavior. Recent works have introduced hybrid RIS structures possessing a few active elements that can sense and digitally process received data. Here, we propose the operation of an entirely autonomous RIS that operates without a control link between the RIS and BS. Using a few sensing elements, the autonomous RIS employs a deep Q network (DQN) based on reinforcement learning in order to enhance the sum rate of the network. Our results illustrate the potential of deploying autonomous RISs in wireless networks with essentially no network overhead.

Related Topics

Reinforcement Learning

Computer Science

Artificial Intelligence

Human–Computer Interaction

Mathematics Education

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Reinforcement learning Computer science Artificial intelligence Autonomous learning Human–computer interaction Psychology Mathematics education

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2403.09270
PDF: https://arxiv.org/pdf/2403.09270
OA Status: green
Related Works: 10
OpenAlex ID: https://openalex.org/W4392873688

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DOI: https://doi.org/10.48550/arxiv.2403.09270

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Title: A Deep Reinforcement Learning Approach for Autonomous Reconfigurable Intelligent Surfaces

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Type: preprint

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Language: en

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Publication year: 2024

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Publication date: 2024-03-14

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Authors: Hyuckjin Choi, Ly V. Nguyen, Junil Choi, A. Lee Swindlehurst

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Landing page: https://arxiv.org/abs/2403.09270

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PDF URL: https://arxiv.org/pdf/2403.09270

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OA URL: https://arxiv.org/pdf/2403.09270

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Concepts: Reinforcement learning, Computer science, Artificial intelligence, Autonomous learning, Human–computer interaction, Psychology, Mathematics education

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Cited by: 0

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Related works (count): 10

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