Generative AI-Driven Semantic Communication Framework for NextG Wireless Network

This work designs a novel semantic communication (SemCom) framework for next-generation wireless networks to tackle high bandwidth consumption, latency, and degraded quality of service (QoS). These challenges, caused by transmitting vast amounts of unnecessary data, hinder real-time applications like intelligent transportation systems (ITS), metaverse, and mixed reality. The proposed framework aims to reduce this communication overhead and maintain QoS for these emerging systems. First, an intelligent semantic transmitter is designed to capturemeaningfulinformation (e.g.,roadsideimage 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 introducedat the receiverfor building the SemComframeworkto reconstruct and denoise the received semantic dataframes. In particular, the Generative Adversarial Network (GAN) mechanism is designed to maintainhigh-qualityreconstruction under different signal-to-noise (SNR) channel conditions. To support edge deployment, a lightweight knowledge-distilled GAN is proposed for efficient reconstruction with minimal quality loss. Finally, we have evaluated the proposed semantic communication (SemCom) frameworkin areal-world 6 G scenario of ITS; in particular, the base station equipped with an RGB camera and a mmWave phased array. Experimental results demonstrate theeffectivenessof the proposed SemCom framework by achieving high-quality reconstruction across various SNR channel conditions, resulting in 89.45% data reduction in communication.