RoNet: Toward Robust Neural Assisted Mobile Network Configuration

TL;DR

RoNet is a framework that enhances the robustness of neural-assisted mobile network configuration, effectively balancing performance and resilience against unpredictable network variabilities.

Contribution

It introduces a novel three-stage training approach combining normal training, adversarial learning, and robust defense for improved network configuration policies.

Findings

RoNet outperforms existing methods in robustness and adaptability.

Extensive NS-3 simulations validate its scalability and effectiveness.

The framework successfully balances performance with robustness in diverse scenarios.

Abstract

Automating configuration is the key path to achieving zero-touch network management in ever-complicating mobile networks. Deep learning techniques show great potential to automatically learn and tackle high-dimensional networking problems. The vulnerability of deep learning to deviated input space, however, raises increasing deployment concerns under unpredictable variabilities and simulation-to-reality discrepancy in real-world networks. In this paper, we propose a novel RoNet framework to improve the robustness of neural-assisted configuration policies. We formulate the network configuration problem to maximize performance efficiency when serving diverse user applications. We design three integrated stages with novel normal training, learn-to-attack, and robust defense method for balancing the robustness and performance of policies. We evaluate RoNet via the NS-3 simulator extensively and the simulation results show that RoNet outperforms existing solutions in terms of robustness, adaptability, and scalability.