Towards Ultra-Reliable 6G: Semantics Empowered Robust Beamforming for Millimeter-Wave Networks

In the rapidly advancing landscape of 6G wireless communication, beamforming plays a crucial role especially with the utilization of millimeter-wave and terahertz frequency bands being pivotal for achieving ultra-high data rates. Despite their promise, these bands present a significant challenge due to the beam training overheads required for precise beamforming, particularly in high-mobility applications like intelligent transportation systems and emerging virtual reality platforms such as the metaverse. While initial deep learning models mitigates the beam training overheads, their performance is compromised due to sensitivity to environmental and lighting conditions, revealing a critical gap in robustness. To address this limitation, this paper proposed a semantic-based method specifically designed to enhance the robustness of beamforming. Utilizing the cutting-edge You Only Look Once version 8 (YOLOv8) algorithm, semantic data from RGB camera images has been extracted to significantly improve the system's adaptability across a range of environmental conditions. Further, to complement the beam management a novel approach is proposed for the identification of target vehicle by employing K-means clustering in conjunction with the GPS data of the target vehicle. For maintaining the ultra reliable low latency communication (URLLC) a lightweight model has been used to predict the optimal beamforming index. The efficacy of our proposed model is empirically substantiated through rigorous experimental trials in real-world 6G environment, demonstrating significant improvements in average received power ranging from 6.49% to 38.27%, compared to the baselines.