|Arjun Anand||The University of Texas, Austin, USA|
|Gustavo De Veciana||The University of Texas at Austin, USA|
|Sanjay Shakkottai||The University of Texas at Austin, USA|
Emerging 5G systems will need to efficiently support both broadband traffic (eMBB) and ultra-low-latency (URLLC) traffic. In these systems, time is divided into slots which are further subdivided into minislots. From a scheduling perspective, eMBB resource allocations occur at slot boundaries, whereas to reduce latency URLLC traffic is pre-emptively overlapped at the minislot timescale, resulting in selective superposition/puncturing of eMBB allocations. This approach enables minimal URLLC latency at a potential rate loss to eMBB traffic. We study joint eMBB and URLLC schedulers for such systems, with the dual objectives of maximizing utility for eMBB traffic while satisfying instantaneous URLLC demands. For a linear rate loss model (loss to eMBB is linear in the amount of superposition/puncturing), we derive an optimal joint scheduler. Somewhat counter-intuitively, our results show that our dual objectives can be met by an iterative gradient scheduler for eMBB traffic that anticipates the expected loss from URLLC traffic, along with an URLLC demand scheduler that is oblivious to eMBB channel states, utility functions and allocations decisions of the eMBB scheduler. Next we consider a more general class of (convex) loss models and study optimal online joint eMBB/URLLC schedulers within the broad class of channel state dependent but time-homogeneous policies. We validate the characteristics and benefits of our schedulers via simulation.