  The evolution of the future beyond-5G/6G networks towards a service-aware
network is based on network slicing technology. With network slicing,
communication service providers seek to meet all the requirements imposed by
the verticals, including ultra-reliable low-latency communication (URLLC)
services. In addition, the open radio access network (O-RAN) architecture paves
the way for flexible sharing of network resources by introducing more
programmability into the RAN. RAN slicing is an essential part of end-to-end
network slicing since it ensures efficient sharing of communication and
computation resources. However, due to the stringent requirements of URLLC
services and the dynamics of the RAN environment, RAN slicing is challenging.
In this article, we propose a two-level RAN slicing approach based on the O-RAN
architecture to allocate the communication and computation RAN resources among
URLLC end-devices. For each RAN slicing level, we model the resource slicing
problem as a single-agent Markov decision process and design a deep
reinforcement learning algorithm to solve it. Simulation results demonstrate
the efficiency of the proposed approach in meeting the desired quality of
service requirements.
