# Experimental Studies on Low-Latency RIS Beam Tracking: Edge-Integrated and Visually Steered
## 3 Results
### Demonstration of the Visually Steered RIS Control Mechanism  
The whole system consists of a communication system module, a target detection and depth mapping module, and an RIS control module, which are responsible for communication signal transmission, user detection and coordinate calculation, and RIS beam adjustment, respectively, so as to realize vision-based RIS beam tracking. This scheme eliminates the feedback mechanism between the receiver and the RIS. YOLOv7 and Intel RealSense D435i are used in this experiment for real-time target detection to recognize the user's position in the scene. The Intel RealSense D435i depth camera is responsible for capturing the RGB image and depth information, and calculating the user's spatial coordinates \((X,Y,Z)\) according to Algorithm 2 through the Grid Mapping mechanism to determine the user's position in the environment. The complete system architecture is shown in Figure 7.  
Figure 5: Experimental setup of the EIR-CM system. (**a**) System diagram illustrating the signal flow, RIS placement, and GPS-based synchronization, etc. (**b**) Photo of the real experimental environment, showing the transmitter, receiver, RIS, control board, and USRP devices, etc.  
Figure 6: Experimental results with the EIR-CM system. (**a**) Initial State: A preconfigured RIS beam in NLOS. (**b**) UE Movement: Signal distortion due to a mismatched beam. (**c**) Beam Adjustment: Optimized RIS reflection for signal recovery.  
As shown in Figure 8a, the camera detects that the target user appears on the left side of the frame and uses the YOLOv7 target detection model to identify its exact location and outputs the bounding box coordinates and confidence score of the detected user. Subsequently, the RIS control system determines the spatial region of the user based on the detected position and selects the corresponding RIS codebook entry to adjust the RIS reflected beam direction, thus ensuring that the signal is enhanced towards the target user. At this point, the receiver obtains a strong signal. The experimental results show that the constellation diagram is clear and the symbol clusters are concentrated, indicating good signal quality and low BER.  
As shown in Figure 8b, when the target user is completely out of the camera's field of view or when absorbing material is used to block the RIS reflective path, the system is unable to detect a valid target. As a result, the RIS is unable to adjust the beam accordingly, resulting in a signal that does not effectively reach the receiver. In this case, the receiver's constellation map is severely distorted. This further validates the critical role played by visual-based perception in the RIS control process.  
Figure 7: Experimental setup of the VSR-CM System. (**a**) System diagram of the VSR-CM architecture, showing the integration of the Jetson Nano and camera for visual sensing and RIS control, etc. (**b**) Photo of the experimental environment, illustrating the transmitter, receiver, RIS, Jetson Nano window, control board, and human target model, etc.  
When the target user moves to the right within the camera's field of view, the system continuously detects the user's position and adjusts the RIS reflective beam accordingly to ensure stable signal transmission to the receiver. The experimental results are shown in Figure 8c. From the constellation diagram of the receiver, it can be observed that, despite the user's movement, the received signal still maintains good quality, with clear and concentrated symbol clusters and a low bit error rate (BER). This indicates that the RIS control system is able to effectively track the user's movement and optimize the signal reflection path in real time.