This work designs a novel semantic communication (SemCom) framework for the next-generation wireless network to tackle the challenges of unnecessary transmission of vast amounts that cause high bandwidth consumption, more latency, and experience with bad quality of services (QoS). In particular, these challenges hinder applications like intelligent transportation systems (ITS), metaverse, mixed reality, and the Internet of Everything, where real-time and efficient data transmission is paramount. Therefore, to reduce communication overhead and maintain the QoS of emerging applications such as metaverse, ITS, and digital twin creation, this work proposes a novel semantic communication framework. First, an intelligent semantic transmitter is designed to capture the meaningful information (e.g., the rode-side image in ITS) by designing a domain-specific Mobile Segment Anything Model (MSAM)-based mechanism to reduce the potential communication traffic while QoS remains intact. Second, the concept of generative AI is introduced for building the SemCom to reconstruct and denoise the received semantic data frame at the receiver end. In particular, the Generative Adversarial Network (GAN) mechanism is designed to maintain a superior quality reconstruction under different signal-to-noise (SNR) channel conditions. Finally, we have tested and evaluated the proposed semantic communication (SemCom) framework with the real-world 6G scenario of ITS; in particular, the base station equipped with an RGB camera and a mmWave phased array. Experimental results demonstrate the efficacy of the proposed SemCom framework by achieving high-quality reconstruction across various SNR channel conditions, resulting in 93.45% data reduction in communication.

翻译：本研究针对下一代无线网络设计了一种新型语义通信框架，以解决海量非必要传输导致的高带宽消耗、高时延及服务质量差等挑战。这些挑战尤其制约着智能交通系统、元宇宙、混合现实和万物互联等对实时高效数据传输至关重要的应用。为此，本文提出一种新型语义通信框架，旨在降低通信开销并维持元宇宙、智能交通系统和数字孪生等新兴应用的服务质量。首先，通过设计基于领域特定移动分割一切模型（MSAM）的机制，构建智能语义发射器以提取有意义信息（如智能交通系统中的路边图像），在保持服务质量不变的前提下减少潜在通信流量。其次，引入生成式AI概念构建语义通信，在接收端对接收到的语义数据帧进行重建与去噪。具体而言，设计了生成对抗网络（GAN）机制，使其能在不同信噪比信道条件下保持优质重建效果。最后，我们在真实6G智能交通场景中对所提语义通信框架进行了测试与评估——该场景中基站配备RGB摄像头与毫米波相控阵。实验结果表明，该语义通信框架能在多种信噪比条件下实现高质量重建，通信数据量减少率达93.45%。