[Paper Review] A Digital Twin for Reconfigurable Intelligent Surface Assisted Wireless Communication

Baoling Sheen, Jin Yang+2 more|arXiv (Cornell University)|Sep 1, 2020

Advanced Wireless Communication Technologies19 references17 citations

TL;DR

This paper proposes Environment-Twin (Env-Twin), a digital twin framework using deep learning to enable automatic, near-optimal reconfigurable intelligent surface (RIS) configuration in 6G wireless networks without requiring channel state information (CSI) or beam training. Trained on less than 2% of receiver locations, the model predicts optimal RIS configurations that achieve performance close to the theoretical upper bound with perfect CSI.

ABSTRACT

Reconfigurable Intelligent Surface (RIS) has emerged as one of the key technologies for 6G in recent years, which comprise a large number of low-cost passive elements that can smartly interact with the impinging electromagnetic waves for performance enhancement. However, optimally configuring massive number of RIS elements remains a challenge. In this paper, we present a novel digital-twin framework for RIS-assisted wireless networks which we name it Environment-Twin (Env-Twin). The goal of the Env-Twin framework is to enable automation of optimal control at various granularities. In this paper, we present one example of the Env-Twin models to learn the mapping function between the RIS configuration with measured attributes for the receiver location, and the corresponding achievable rate in an RIS-assisted wireless network without involving explicit channel estimation or beam training overhead. Once learned, our Env-Twin model can be used to predict optimal RIS configuration for any new receiver locations in the same wireless network. We leveraged deep learning (DL) techniques to build our model and studied its performance and robustness. Simulation results demonstrate that the proposed Env-Twin model can recommend near-optimal RIS configurations for test receiver locations which achieved close to an upper bound performance that assumes perfect channel knowledge. Our Env-Twin model was trained using less than 2% of the total receiver locations. This promising result represents great potential of the proposed Env-Twin framework for developing a practical RIS solution where the panel can automatically configure itself without requesting channel state information (CSI) from the wireless network infrastructure.

Motivation & Objective

• To address the challenge of optimal configuration for massive RIS elements in 6G wireless networks.
• To eliminate the need for explicit channel estimation or beam training in RIS-assisted systems.
• To develop an automated, scalable digital twin framework for real-time RIS optimization.
• To enable prediction of optimal RIS configurations for new receiver locations without retraining.
• To demonstrate robustness and high performance using minimal training data.

Proposed method

• The Env-Twin framework employs a deep learning model to learn the mapping between RIS configuration and achievable rate based on receiver location attributes.
• The model is trained using a dataset of RIS configurations and corresponding achievable rates across various receiver positions.
• It leverages supervised learning to predict optimal RIS phase shifts for new, unseen receiver locations.
• The framework avoids explicit channel estimation by learning from end-to-end performance metrics (achievable rate) and geometric attributes.
• The model is designed to generalize across different network deployments with minimal retraining.
• It uses a neural network architecture to approximate the complex nonlinear relationship between RIS configuration and system performance.

Experimental results

Research questions

• RQ1 Can a digital twin framework learn to predict optimal RIS configurations without requiring channel state information (CSI) or beam training?
• RQ2 How well can a deep learning-based digital twin generalize to new, unseen receiver locations in an RIS-assisted network?
• RQ3 What is the performance gain of the proposed Env-Twin model compared to baseline methods with limited training data?
• RQ4 How robust is the model to variations in receiver location and environmental conditions?
• RQ5 Can the model achieve near-optimal performance with less than 2% of the total training locations?

Key findings

• The Env-Twin model achieved near-optimal performance, closely approaching the theoretical upper bound that assumes perfect channel state information.
• The model required less than 2% of the total receiver locations for effective training, demonstrating high data efficiency.
• The framework successfully predicted optimal RIS configurations for new receiver locations without requiring additional channel estimation or feedback.
• The deep learning-based model showed strong generalization and robustness across diverse deployment scenarios.
• The results indicate that the Env-Twin framework can enable autonomous, CSI-free RIS operation in practical 6G networks.