Millimeter-wave (mmWave) networks offer the potential for high-speed data transfer and precise localization, leveraging large antenna arrays and extensive bandwidths. However, these networks are challenged by significant path loss and susceptibility to blockages. In this study, we delve into the use of situational awareness for beam prediction within the 5G NR beam management framework. We introduce an analytical framework based on the Cram\'{e}r-Rao Lower Bound, enabling the quantification of 6D position-related information of geometric reflectors. This includes both 3D locations and 3D orientation biases, facilitating accurate determinations of the beamforming gain achievable by each reflector or candidate beam. This framework empowers us to predict beam alignment performance at any given location in the environment, ensuring uninterrupted wireless access. Our analysis offers critical insights for choosing the most effective beam and antenna module strategies, particularly in scenarios where communication stability is threatened by blockages. Simulation results show that our approach closely approximates the performance of an ideal, Oracle-based solution within the existing 5G NR beam management system.

翻译：毫米波网络凭借大规模天线阵列和宽带宽，具有高速数据传输和精确定位的潜力，但面临严重的路径损耗和易受遮挡的挑战。本研究探讨了在5G NR波束管理框架内利用情境感知进行波束预测的方法。我们引入了一种基于克拉美罗下界的分析框架，能够量化几何反射体的六维位置相关信息，包括三维位置和三维方向偏差，从而精确确定每个反射体或候选波束可实现的天线增益。该框架使我们能够预测环境中任意位置的波束对齐性能，确保无线接入不中断。我们的分析为选择最有效的波束和天线模块策略提供了关键见解，尤其在通信稳定性可能因遮挡而受损的场景中。仿真结果表明，在现有5G NR波束管理系统内，本方法的性能接近基于理想先知方案的解决方案。