Literature Database Entry

basaran2026veriran

Osman Tugay Basaran and Falko Dressler, "VeriRAN: Explainable and Runtime-Verified Multi-Agent Control for Trustworthy AI-RAN," Proceedings of 32nd ACM International Conference on Mobile Computing and Networking (MobiCom 2026), 3rd ACM workshop on Open and AI RAN (Open-AI RAN 2026), Poster Session, Austin, TX, October 2026. (to appear)

Abstract

Artificial intelligence (AI)-Native sixth-generation (6G) radio access networks (RANs) are expected to support increasingly dynamic control loops, where learning-based agents continuously adapt radio resources, slice priorities, and service behavior. However, this shift from static optimization to autonomous control raises a fundamental trust question: how can AI-driven RAN decisions remain safe when radio conditions change, telemetry becomes unreliable, or latency-critical services experience sudden stress? This paper presents VeriRAN, a lightweight runtime-verified AI-RAN control architecture that separates intelligence from authorization. Instead of treating AI agents as trusted actuators, VeriRAN uses them as intelligent action proposers that generate radio-aware and service-level-agreement (SLA)-aware decisions. Our verification shield is placed directly in the control path to approve, constrain, or replace unsafe actions before they affect the RAN.

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Osman Tugay Basaran
Falko Dressler

BibTeX reference

@inproceedings{basaran2026veriran,
    author = {Basaran, Osman Tugay and Dressler, Falko},
    note = {to appear},
    title = {{VeriRAN: Explainable and Runtime-Verified Multi-Agent Control for Trustworthy AI-RAN}},
    publisher = {ACM},
    address = {Austin, TX},
    booktitle = {32nd ACM International Conference on Mobile Computing and Networking (MobiCom 2026), 3rd ACM workshop on Open and AI RAN (Open-AI RAN 2026), Poster Session},
    month = {10},
    year = {2026},
   }
   
   

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Last modified: 2026-08-31