Advancements in 6G wireless technology have elevated the importance of beamforming, especially for attaining ultra-high data rates via millimeter-wave (mmWave) frequency deployment. Although promising, mmWave bands require substantial beam training to achieve precise beamforming. While initial deep learning models that use RGB camera images demonstrated promise in reducing beam training overhead, their performance suffers due to sensitivity to lighting and environmental variations. Due to this sensitivity, Quality of Service (QoS) fluctuates, eventually affecting the stability and dependability of networks in dynamic environments. This emphasizes a critical need for more robust solutions. This paper proposes a robust beamforming technique to ensure consistent QoS under varying environmental conditions. An optimization problem has been formulated to maximize users' data rates. To solve the formulated NP-hard optimization problem, we decompose it into two subproblems: the semantic localization problem and the optimal beam selection problem. To solve the semantic localization problem, we propose a novel method that leverages the k-means clustering and YOLOv8 model. To solve the beam selection problem, we propose a novel lightweight hybrid architecture that utilizes various data sources and a weighted entropy-based mechanism to predict the optimal beams. Rapid and accurate beam predictions are needed to maintain QoS. A novel metric, Accuracy-Complexity Efficiency (ACE), has been proposed to quantify this. Six testing scenarios have been developed to evaluate the robustness of the proposed model. Finally, the simulation result demonstrates that the proposed model outperforms several state-of-the-art baselines regarding beam prediction accuracy, received power, and ACE in the developed test scenarios.

翻译：6G无线技术的进步提升了波束赋形的重要性，尤其是在通过部署毫米波（mmWave）频段实现超高速率方面。尽管前景广阔，毫米波频段需要大量的波束训练以实现精确的波束赋形。虽然早期使用RGB相机图像的深度学习模型在降低波束训练开销方面显示出潜力，但其性能因对光照和环境变化的敏感性而受限。由于这种敏感性，服务质量（QoS）会发生波动，最终影响动态环境中网络的稳定性和可靠性。这凸显了对更鲁棒解决方案的迫切需求。本文提出了一种鲁棒的波束赋形技术，以确保在变化的环境条件下保持一致的QoS。我们构建了一个优化问题以最大化用户数据速率。为解决这个已构建的NP难优化问题，我们将其分解为两个子问题：语义定位问题和最优波束选择问题。为解决语义定位问题，我们提出了一种利用k-means聚类和YOLOv8模型的新方法。为解决波束选择问题，我们提出了一种新颖的轻量级混合架构，该架构利用多种数据源和基于加权熵的机制来预测最优波束。维持QoS需要快速且准确的波束预测。为此，我们提出了一个新指标——精度-复杂度效率（ACE）——来量化这一需求。我们开发了六种测试场景以评估所提模型的鲁棒性。最终，仿真结果表明，在所开发的测试场景中，所提模型在波束预测精度、接收功率和ACE方面均优于多个先进的基线方法。