Integrated Imaging and Communication with Reconfigurable Intelligent Surfaces

TL;DR

The paper tackles reducing RIS beam training overhead in RIS-aided integrated imaging and communication by exploiting RIS-based scene depth estimation. It introduces a depth-map–driven pipeline that detects the user via background subtraction and DBSCAN, then designs the RIS communication vector either directly from estimated angles or by selecting beams from a codebook, thereby lowering overhead. Through ray-traced simulations at 60 GHz, the approach achieves beamforming gains close to exhaustive search while reducing training iterations by roughly three orders of magnitude, illustrating the viability of imaging-aided RIS for ISAC. The work demonstrates a practical integration of FMCW sensing with RIS-based communication, leveraging depth maps to enable efficient, high-resolution, and low-overhead beam management.

Abstract

Reconfigurable intelligent surfaces, with their large number of antennas, offer an interesting opportunity for high spatial-resolution imaging. In this paper, we propose a novel RIS-aided integrated imaging and communication system that can reduce the RIS beam training overhead for communication by leveraging the imaging of the surrounding environment. In particular, using the RIS as a wireless imaging device, our system constructs the scene depth map of the environment, including the mobile user. Then, we develop a user detection algorithm that subtracts the background and extracts the mobile user attributes from the depth map. These attributes are then utilized to design the RIS interaction vector and the beam selection strategy with low overhead. Simulation results show that the proposed approach can achieve comparable beamforming gain to the optimal/exhaustive beam selection solution while requiring 1000 times less beam training overhead.

Figures (4)

• Figure 1: This figure shows the integrated imaging and communication system. In the imaging/sensing stage, the wireless sensing unit transmits the sensing signals to the RIS via a feeding antenna. The sensing signals are reflected towards the environment by the RIS, which then reflects the backscattered/reflected signals back to the wireless sensing unit. The received signals are processed by the wireless sensing unit to construct a depth map of the environment, which enables the system to design the RIS interaction vector for communication.
• Figure 2: This figure presents the operation flow of the proposed image-aided communication solution. To begin, the estimated depth map undergoes background subtraction, followed by the elimination of undesired reflections and sensing noise. The pixel coordinates and the corresponding angles of the user can then be obtained from the processed depth map. Finally, the beam selection strategy is performed based on the estimated angles of the user.
• Figure 3: This figure depicts the adopted indoor scenario. We place the user in the NLoS area (red rectangle) to ensure that there is no direct link between the AP and the UE.
• Figure 4: This figure presents the beamforming gain provided by the selected top-k beams, compared to the equal-gain beamforming (upperbound) and the exhaustive search. The oversampled codebook is generated with the oversampling factors (OSFs) of four in azimuth and elevation dimensions.