  Interference continues to be a key limiting factor in cellular radio access
network (RAN) deployments. Effective, data-driven, self-adapting radio resource
management (RRM) solutions are essential for tackling interference, and thus
achieving the desired performance levels particularly at the cell-edge. In
future network architecture, RAN intelligent controller (RIC) running with
near-real-time applications, called xApps, is considered as a potential
component to enable RRM. In this paper, based on deep reinforcement learning
(RL) xApp, a joint sub-band masking and power management is proposed for smart
interference management. The sub-band resource masking problem is formulated as
a Markov Decision Process (MDP) that can be solved employing deep RL to
approximate the policy functions as well as to avoid extremely high
computational and storage costs of conventional tabular-based approaches. The
developed xApp is scalable in both storage and computation. Simulation results
demonstrate advantages of the proposed approach over decentralized baselines in
terms of the trade-off between cell-centre and cell-edge user rates, energy
efficiency and computational efficiency.
