Experimental Studies on Low-Latency RIS Beam Tracking: Edge-Integrated and Visually Steered
Abstract
1. Introduction
- 1.
- The Edge-Integrated RIS Control Mechanism (EIR-CM). This approach integrates the UE baseband signal processing and the RIS control system into the same edge server. It migrates the RIS feedback computation logic to a local computing unit, eliminating dependence on remote servers for codebook management. Within the Local Area Network (LAN), the UE directly controls the RIS reflection mode via TCP, bypassing remote server interactions. By adopting an integrated RIS control architecture, real-time CSI computation and RIS feedback optimization are performed on the local host, reducing beam adjustment latency by over 50% compared to traditional remote interaction schemes. This approach is suitable for low-latency, high-speed dynamic RIS tracking scenarios, particularly in applications such as smart buildings, industrial automation, and intelligent transportation, serving as a low-latency RIS control solution for 6G networks.
- 2.
- The Visually Steered RIS Control Mechanism (VSR-CM). This approach employs the YOLOv7 object detection algorithm [31] + depth camera (Intel RealSense D435i) for user detection, enabling real-time UE position recognition. Unlike traditional methods that traverse the entire codebook, this scheme directly maps the detected user position to the optimal RIS codebook via object detection, significantly accelerating beam adjustment. As a result, no feedback from the UE to the RIS control system is required, and RIS control is performed purely based on vision perception, reducing feedback communication overhead. This approach is particularly suitable for high-speed mobile users (such as drones, autonomous driving, and V2X vehicular networks) and enables millisecond-level dynamic RIS beam adjustments, improving tracking stability.
2. System Architecture
2.1. The Edge-Integrated RIS Control Mechanism
Algorithm 1 The EIR-CM mode tracking algorithm |
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2.2. Visually Steered RIS Control Mechanism
Algorithm 2 The VSR-CM mode tracking algorithm |
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- 1.
- Pre-processing stage: First, the system initializes the YOLOv7 target detection model, loads the pre-trained YOLOv7 model, and starts the RGB and depth data streams from the Intel RealSense D435i camera; the RGB images are used for target detection, while the depth information is used for the subsequent spatial coordinate calculation. In addition, the system initializes UART communication to establish a serial connection between the RIS control system and the FPGA control board for transmitting beam control commands. Finally, the system loads the RIS reflection codebook (Patterns.txt), which stores the RIS reflection patterns in different directions. The system will select the appropriate beam direction according to the detected user position.
- 2.
- Object Detection and User Localization: The camera starts capturing environmental images, and the YOLOv7 model processes the input RGB image to output object detection results, including bounding box coordinates , where represents the top-left corner of the detected object, and represents the bottom-right corner (as shown in Figure 4). YOLOv7 also classifies detected targets and provides a confidence score to ensure detection reliability. For RIS beam control, the system divides the image level into 8 grid cells (cells) and calculates the cell width:cell_width = frame_width/8.
3. Results
3.1. Demonstration of the Edge-Integrated RIS Control Mechanism
3.2. Demonstration of the Visually Steered RIS Control Mechanism
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
List of Acronyms
Acronym | Definition |
RIS | Reconfigurable Intelligent Surface |
UE | User Equipment |
EIR-CM | Edge-Integrated RIS Control Mechanism |
VSR-CM | Visually Steered RIS Control Mechanism |
mmWave | Millimeter–Wave |
LoS/NLoS | Line-of-Sight/Non-Line-of-Sight |
LAN | Local Area Network |
V2X | Vehicle-to-Everything |
UAV | Unmanned Aerial Vehicle |
TCP/IP | Transmission Control Protocol/Internet Protocol |
FPGA | Field-Programmable Gate Array |
CSI | Channel State Information |
YOLOv7 | You Only Look Once version 7 (real-time object detector) |
UART | Universal Asynchronous Receiver/Transmitter |
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Tracking Scheme | Traditional Multi-Host Scheme [28] | Edge-Integrated RIS Control Mechanism | Visually Steered RIS Control Mechanism |
---|---|---|---|
Tracking Latency | 3–4 s | 1–2 s | <0.5 s |
Synchronization | Required | No Required | No Required |
Feedback | Required | Required | No Required |
Codebook Traversal | Traverse All | Traverse All | Directly Select Target Codebook |
System Complexity | Multi-Host | Integrated Host | Single Host + Depth Camera |
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Wang, Z.; Nie, Y. Experimental Studies on Low-Latency RIS Beam Tracking: Edge-Integrated and Visually Steered. Network 2025, 5, 22. https://doi.org/10.3390/network5030022
Wang Z, Nie Y. Experimental Studies on Low-Latency RIS Beam Tracking: Edge-Integrated and Visually Steered. Network. 2025; 5(3):22. https://doi.org/10.3390/network5030022
Chicago/Turabian StyleWang, Zekai, and Yuming Nie. 2025. "Experimental Studies on Low-Latency RIS Beam Tracking: Edge-Integrated and Visually Steered" Network 5, no. 3: 22. https://doi.org/10.3390/network5030022
APA StyleWang, Z., & Nie, Y. (2025). Experimental Studies on Low-Latency RIS Beam Tracking: Edge-Integrated and Visually Steered. Network, 5(3), 22. https://doi.org/10.3390/network5030022