A collaborative statistical actor-critic learning approach for 6G network slicing control
10.1109/GLOBECOM46510.2021.9685218
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/367515
Tipus de documentComunicació de congrés
Data publicació2021
EditorInstitute of Electrical and Electronics Engineers (IEEE)
Condicions d'accésAccés obert
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Abstract
Artificial intelligence (AI)-driven zero-touch massive network slicing is envisioned to be a disruptive technology in beyond 5G (B5G)/6G, where tenancy would be extended to the final consumer in the form of advanced digital use-cases. In this paper, we propose a novel model-free deep reinforcement learning (DRL) framework, called collaborative statistical Actor-Critic (CS-AC) that enables a scalable and farsighted slice performance management in a 6G-like RAN scenario that is built upon mobile edge computing (MEC) and massive multiple-input multiple-output (mMIMO). In this intent, the proposed CS-AC targets the optimization of the latency cost under a long-term statistical service-level agreement (SLA). In particular, we consider the Q-th delay percentile SLA metric and enforce some slice-specific preset constraints on it. Moreover, to implement distributed learners, we propose a developed variant of soft Actor-Critic (SAC) with less hyperparameter sensitivity. Finally, we present numerical results to showcase the gain of the adopted approach on our built OpenAI-based network slicing environment and verify the performance in terms of latency, SLA Q-th percentile, and time efficiency. To the best of our knowledge, this is the first work that studies the feasibility of an AI-driven approach for massive network slicing under statistical SLA.
CitacióRezazadeh, F. [et al.]. A collaborative statistical actor-critic learning approach for 6G network slicing control. A: IEEE Global Communications Conference. "2021 IEEE Global Communications Conference (GLOBECOM): Madrid, Spain 7-11 December 2021: proceedings". Institute of Electrical and Electronics Engineers (IEEE), 2021, ISBN 978-1-7281-8104-2. DOI 10.1109/GLOBECOM46510.2021.9685218.
ISBN978-1-7281-8104-2
Versió de l'editorhttps://ieeexplore.ieee.org/document/9685218
Col·leccions
- Doctorat en Teoria del Senyal i Comunicacions - Ponències/Comunicacions de congressos [236]
- WiComTec - Grup de recerca en Tecnologies i Comunicacions Sense Fils - Ponències/Comunicacions de congressos [176]
- Departament de Teoria del Senyal i Comunicacions - Ponències/Comunicacions de congressos [3.327]
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