  In this paper, we study the resource slicing problem in a dynamic
multiplexing scenario of two distinct 5G services, namely Ultra-Reliable Low
Latency Communications (URLLC) and enhanced Mobile BroadBand (eMBB). While eMBB
services focus on high data rates, URLLC is very strict in terms of latency and
reliability. In view of this, the resource slicing problem is formulated as an
optimization problem that aims at maximizing the eMBB data rate subject to a
URLLC reliability constraint, while considering the variance of the eMBB data
rate to reduce the impact of immediately scheduled URLLC traffic on the eMBB
reliability. To solve the formulated problem, an optimization-aided Deep
Reinforcement Learning (DRL) based framework is proposed, including: 1) eMBB
resource allocation phase, and 2) URLLC scheduling phase. In the first phase,
the optimization problem is decomposed into three subproblems and then each
subproblem is transformed into a convex form to obtain an approximate resource
allocation solution. In the second phase, a DRL-based algorithm is proposed to
intelligently distribute the incoming URLLC traffic among eMBB users.
Simulation results show that our proposed approach can satisfy the stringent
URLLC reliability while keeping the eMBB reliability higher than 90%.
