Secrecy Preserving in Stochastic Resource Orchestration for Multi-Tenancy Network Slicing

TL;DR

This paper proposes a deep reinforcement learning approach to optimize resource allocation in multi-tenant RAN slicing, ensuring secrecy and efficiency amidst competition and eavesdropping threats.

Contribution

It introduces a stochastic game model for multi-tenant RAN slicing with secrecy considerations and develops a deep RL scheme to approximate Nash equilibrium solutions.

Findings

The proposed scheme outperforms baseline methods in average utility per MU.

Deep RL effectively approximates optimal control policies in complex stochastic games.

Secrecy-preserving resource allocation enhances security in multi-tenant network slicing.

Abstract

Network slicing is a proposing technology to support diverse services from mobile users (MUs) over a common physical network infrastructure. In this paper, we consider radio access network (RAN)-only slicing, where the physical RAN is tailored to accommodate both computation and communication functionalities. Multiple service providers (SPs, i.e., multiple tenants) compete with each other to bid for a limited number of channels across the scheduling slots, aiming to provide their subscribed MUs the opportunities to access the RAN slices. An eavesdropper overhears data transmissions from the MUs. We model the interactions among the non-cooperative SPs as a stochastic game, in which the objective of a SP is to optimize its own expected long-term payoff performance. To approximate the Nash equilibrium solutions, we first construct an abstract stochastic game using the channel auction outcomes. Then we linearly decompose the per-SP Markov decision process to simplify the decision-makings and derive a deep reinforcement learning based scheme to approach the optimal abstract control policies. TensorFlow-based experiments verify that the proposed scheme outperforms the three baselines and yields the best performance in average utility per MU per scheduling slot.