  In this paper, we propose a null-space-based preemptive scheduling framework
for cross-objective optimization to always guarantee robust URLLC performance,
while extracting the maximum possible eMBB capacity. The proposed scheduler
perpetually grants incoming URLLC traffic a higher priority for instant
scheduling. In case that radio resources are not immediately schedulable,
proposed scheduler forcibly enforces an artificial spatial user separation, for
the URLLC traffic to get instantly scheduled over shared resources with ongoing
eMBB transmissions. A pre-defined reference spatial subspace is constructed for
which scheduler instantly picks the active eMBB user whose precoder is the
closest possible. Then, it projects the eMBB precoder on-the-go onto the
reference subspace, in order for its paired URLLC user to orient its decoder
matrix into one possible null space of the reference subspace. Hence, a robust
decoding ability is always preserved at the URLLC user, while cross-maximizing
the ergodic capacity. Compared to the state-of-the-art proposals from industry
and academia, proposed scheduler shows extreme URLLC latency robustness with
significantly improved overall spectral efficiency. Analytical analysis and
extensive system level simulations are presented to support paper conclusions.
