RoNet: Toward Robust Neural Assisted Mobile Network Configuration

Document Type

Conference Proceeding

Publication Date

1-1-2023

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.

Identifier

85178273610 (Scopus)

ISBN

[9781538674628]

Publication Title

IEEE International Conference on Communications

External Full Text Location

https://doi.org/10.1109/ICC45041.2023.10279414

ISSN

15503607

First Page

3878

Last Page

3883

Volume

2023-May

Grant

2212050

Fund Ref

National Science Foundation

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