Importance-Aware Image Segmentation-based Semantic Communication for Autonomous Driving

This article studies the problem of image segmentation-based semantic communication in autonomous driving. In real traffic scenes, detecting the key objects (e.g., vehicles, pedestrians and obstacles) is more crucial than that of other objects to guarantee driving safety. Therefore, we propose a vehicular image segmentation-oriented semantic communication system, termed VIS-SemCom, where image segmentation features of important objects are transmitted to reduce transmission redundancy. First, to accurately extract image semantics, we develop a semantic codec based on Swin Transformer architecture, which expands the perceptual field thus improving the segmentation accuracy. Next, we propose a multi-scale semantic extraction scheme via assigning the number of Swin Transformer blocks for diverse resolution features, thus highlighting the important objects' accuracy. Furthermore, the importance-aware loss is invoked to emphasize the important objects, and an online hard sample mining (OHEM) strategy is proposed to handle small sample issues in the dataset. Experimental results demonstrate that the proposed VIS-SemCom can achieve a coding gain of nearly 6 dB with a 60% mean intersection over union (mIoU), reduce the transmitted data amount by up to 70% with a 60% mIoU, and improve the segmentation intersection over union (IoU) of important objects by 4%, compared to traditional transmission scheme.

Near-Field Beam Training of Intelligent Reflecting Surface: A Novel Two-Layer Codebook

This paper investigates the codebook based near-field beam training of Intelligent Reflecting Surface (IRS). In the considered model, near-field beam training should be performed to focus the signals at the location of user equipment (UE) to obtain the prominent IRS array gain. However, existing codebook schemes can not realize low training overhead and high receiving power, simultaneously. To tackle this issue, a novel two-layer codebook is proposed. Specifically, the layer-1 codebook is designed based on the omnidirectivity of random-phase beam pattern, which estimates the UE distance with training overhead equivalent to that of a DFT codeword. Then, based on the estimated distance of UE, the layer-2 codebook is generated to scan the candidate locations of UE, and finally obtain the optimal codeword for IRS beamforming. Numerical results show that, compared with the benchmarks, the proposed codebook scheme makes more accurate estimation of UE distances and angles, achieving higher date rate, yet with a smaller training overhead.