Beam Foreseeing in Millimeter-Wave Systems with Situational Awareness: Fundamental Limits via Cram\'{e}r-Rao Lower Bound
arxiv(2023)
摘要
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.
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