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

Abstract—Network slicing (NwS) is one of the main technologies

in the €…h-generation of mobile communication and

beyond (5G+). One of the important challenges in the NwS

is information uncertainty which mainly involves demand

and channel state information (CSI). Demand uncertainty is

divided into three types: number of users requests, amount

of bandwidth, and requested virtual network functions workloads.

Moreover, the CSI uncertainty is modeled by three

methods: worst-case, probabilistic, and hybrid. In this paper,

our goal is to maximize the utility of the infrastructure

provider by exploiting deep reinforcement learning algorithms

in end-to-end NwS resource allocation under demand

and CSI uncertainties. ‡e proposed formulation is a nonconvex

mixed-integer non-linear programming problem. To

perform robust resource allocation in problems that involve

uncertainty, we need a history of previous information. To

this end, we use a recurrent deterministic policy gradient

(RDPG) algorithm, a recurrent and memory-based approach

in deep reinforcement learning. ‡en, we compare the RDPG

method in di‚erent scenarios with so… actor-critic (SAC),

deep deterministic policy gradient (DDPG), distributed, and

greedy algorithms. ‡e simulation results show that the SAC

method is better than the DDPG, distributed, and greedy

methods, respectively. Moreover, the RDPG method out performs

the SAC approach on average by 70%.

Index Terms— End-to-end network slicing, Resource allocation,

So…ware-de€ned networking (SDN), Network function

virtualization (NFV), Demand uncertainty, Channel state information

(CSI) uncertainty, Recurrent deterministic policy

gradient (RDPG).