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Millimeter-wave (mmWave) networks offer the potential for high-speed datatransfer and precise localization, leveraging large antenna arrays andextensive bandwidths. However, these networks are challenged by significantpath loss and susceptibility to blockages. In this study, we delve into the useof situational awareness for beam prediction within the 5G NR beam managementframework. We introduce an analytical framework based on the Cram\'{e}r-RaoLower Bound, enabling the quantification of 6D position-related information ofgeometric reflectors. This includes both 3D locations and 3D orientationbiases, facilitating accurate determinations of the beamforming gain achievableby each reflector or candidate beam. This framework empowers us to predict beamalignment performance at any given location in the environment, ensuringuninterrupted wireless access. Our analysis offers critical insights forchoosing the most effective beam and antenna module strategies, particularly inscenarios where communication stability is threatened by blockages. Simulationresults show that our approach closely approximates the performance of anideal, Oracle-based solution within the existing 5G NR beam management system.

Beam Foreseeing in Millimeter-Wave Systems with Situational Awareness: Fundamental Limits via Cram\'{e}r-Rao Lower Bound