Codes of paper: AI-based Robust Resource Allocation in End-to-End Network Slicing under Demand and CSI Uncertainties

Abstract—Network slicing (NwS) is one of the main technologiesin the €…h-generation of mobile communication andbeyond (5G+). One of the important challenges in the NwSis information uncertainty which mainly involves demandand channel state information (CSI). Demand uncertainty isdivided into three types: number of users requests, amountof bandwidth, and requested virtual network functions workloads.Moreover, the CSI uncertainty is modeled by threemethods: worst-case, probabilistic, and hybrid. In this paper,our goal is to maximize the utility of the infrastructureprovider by exploiting deep reinforcement learning algorithmsin end-to-end NwS resource allocation under demandand CSI uncertainties. ‡e proposed formulation is a nonconvexmixed-integer non-linear programming problem. Toperform robust resource allocation in problems that involveuncertainty, we need a history of previous information. Tothis end, we use a recurrent deterministic policy gradient(RDPG) algorithm, a recurrent and memory-based approachin deep reinforcement learning. ‡en, we compare the RDPGmethod in di‚erent scenarios with so… actor-critic (SAC),deep deterministic policy gradient (DDPG), distributed, andgreedy algorithms. ‡e simulation results show that the SACmethod is better than the DDPG, distributed, and greedymethods, respectively. Moreover, the RDPG method out performsthe SAC approach on average by 70%.Index Terms— End-to-end network slicing, Resource allocation,So…ware-de€ned networking (SDN), Network functionvirtualization (NFV), Demand uncertainty, Channel state information(CSI) uncertainty, Recurrent deterministic policygradient (RDPG).