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Radio access network (RAN) slicing is a key technology that enables 5Gnetwork to support heterogeneous requirements of generic services, namelyultra-reliable low-latency communication (URLLC) and enhanced mobile broadband(eMBB). In this paper, we propose a two time-scales RAN slicing mechanism tooptimize the performance of URLLC and eMBB services. In a large time-scale, anSDN controller allocates radio resources to gNodeBs according to therequirements of the eMBB and URLLC services. In a short time-scale, each gNodeBallocates its available resources to its end-users and requests, if needed,additional resources from adjacent gNodeBs. We formulate this problem as anon-linear binary program and prove its NP-hardness. Next, for each time-scale,we model the problem as a Markov decision process (MDP), where the large-timescale is modeled as a single agent MDP whereas the shorter time-scale ismodeled as a multi-agent MDP. We leverage the exponential-weight algorithm forexploration and exploitation (EXP3) to solve the single-agent MDP of the largetime-scale MDP and the multi-agent deep Q-learning (DQL) algorithm to solve themulti-agent MDP of the short time-scale resource allocation. Extensivesimulations show that our approach is efficient under different networkparameters configuration and it outperforms recent benchmark solutions.

Dynamic SDN-based Radio Access Network Slicing with Deep Reinforcement Learning for URLLC and eMBB Services