  Obtaining accurate and timely channel state information (CSI) is a
fundamental challenge for large antenna systems. Mobile systems like 5G use a
beam management framework that joins the initial access, beamforming, CSI
acquisition, and data transmission. The design of codebooks for these stages,
however, is challenging due to their interrelationships, varying array sizes,
and site-specific channel and user distributions. Furthermore, beam management
is often focused on single-sector operations while ignoring the overarching
network- and system-level optimization. In this paper, we proposed an
end-to-end learned codebook design algorithm, network beamspace learning (NBL),
that captures and optimizes codebooks to mitigate interference while maximizing
the achievable performance with extremely large hybrid arrays. The proposed
algorithm requires limited shared information yet designs codebooks that
outperform traditional codebooks by over 10dB in beam alignment and achieve
more than 25% improvements in network spectral efficiency.
