Indoor Signal Focusing with Deep Learning Designed Reconfigurable Intelligent Surfaces

Abstract: Reconfigurable Intelligent Surfaces (RISs) comprised of tunable unit elements have been recently considered in indoor communication environments for focusing signal reflections to intended user locations. However, the current proofs of concept require complex operations for the RIS configuration, which are mainly realized via wired control connections. In this paper, we present a deep learning method for efficient online wireless configuration of RISs when deployed in indoor communication environments. According to the proposed method, a database of coordinate fingerprints is implemented during an offline training phase. This fingerprinting database is used to train the weights and bias of a properly designed Deep Neural Network (DNN), whose role is to unveil the mapping between the measured coordinate information at a user location and the configuration of the RIS's unit cells that maximizes this user's received signal strength. During the online phase of the presented method, the trained DNN is fed with the measured position information at the target user to output the optimal phase configurations of the RIS for signal power focusing on this intended location. Our realistic simulation results using ray tracing on a three dimensional indoor environment demonstrate that the proposed DNN-based configuration method exhibits its merits for all considered cases, and effectively increases the achievable throughput at the target user location.

• The paper introduces a method that integrates deep learning with RIS to eliminate complex wired control setups.
• It employs a two-phase approach, using offline training to map user positions to optimal RIS configurations and an online phase for real-time signal focusing.
• Simulation results in a 3D office environment demonstrate significantly improved throughput and precision in indoor signal delivery.

Indoor Signal Focusing with Deep Learning Designed Reconfigurable Intelligent Surfaces

The paper addresses a critical challenge in indoor wireless communications: achieving effective signal focusing via Reconfigurable Intelligent Surfaces (RISs). These RISs, consisting of tunable unit elements, provide a promising alternative to traditional hardware like additional base stations (BSs) or multiple relays, both of which come with significant economic and operational challenges, especially for indoor environments. One novel approach presented in the paper is leveraging deep learning to configure RISs wirelessly, thus facilitating efficient signal management indoors.

Summary of Methodology

The authors propose a deep learning-based method to configure RISs, eliminating the need for complex manual operations typically required by wired control connections. The methodology includes two phases: an offline training phase and an online application phase.

• Offline Training Phase: A database containing coordinate fingerprints is established. Each fingerprint associates positional information with the optimal RIS configurations for that location. This database is used to train a Deep Neural Network (DNN) by learning the mapping between user position information and the RIS unit configuration that maximizes signal strength at that position.
• Online Application Phase: Once trained, the DNN receives the estimated user position during the communication session and outputs the optimal RIS configuration for enhanced signal focusing. This approach significantly facilitates wireless RIS configuration by reducing the processing complexity and achieving enhanced signal delivery to desired locations within a 3D space.

Technical Evaluation

The paper provides a realistic simulation setup using ray tracing in a three-dimensional (3D) office environment. The results indicate that the DNN-based RIS configuration method increases throughput at the target user location across various scenarios. The simulation demonstrates that leveraging RISs with deep learning substantially boosts signal focusing compared to non-RIS scenarios, supporting higher achievable rates for users located within the specified deployment environment.

Implications and Future Directions

The implementation of the proposed deep learning method for RIS configuration can lead to significant practical and theoretical implications for indoor wireless networks. Practically, it means potentially more economical and efficient deployment strategies for enhancing indoor coverage without requiring additional physical infrastructure. Theoretically, it encourages new developments and investigations into wireless channel modeling, machine learning integration in communication systems, and RIS optimization strategies.

Potential Future Directions:

1. Generalization Across Deployments: Future research can explore ways to enhance the DNN's generalization capabilities across different indoor environments without the need for extensive retraining.
2. Energy Efficiency: Exploration into optimizing the energy consumption associated with implementing RISs in various indoor scenarios.
3. Complex Environments: Expanding the method’s applicability to more complex indoor layouts with diverse obstructions can be considered.
4. Hybrid Models: Developing hybrid models combining classical optimization techniques with deep learning to enhance reliability and performance further.

In conclusion, by integrating deep learning with RIS technology, the paper contributes to the understanding and potential realization of intelligent environments capable of precise signal focus and distribution. This work opens up new possibilities for indoor network design, administration, and optimization, providing a foundation for future technological advancements in the area of smart communications infrastructure.