Published June 23, 2021 | Version v1
Conference paper Open

Actor-Critic-Based Learning for Zero-touch Joint Resource and Energy Control in Network Slicing

  • 1. CTTC Catalan Telecommunications Technology Centre CTTC Catalan Telecommunications Technology Centre


To harness the full potential of beyond 5G (B5G) communication systems, zero-touch network slicing (NS) is viewed as a promising fully-automated management and orchestration (MANO) system. This paper proposes a novel knowledge plane (KP)-based MANO framework that accommodates and exploits recent NS technologies and is termed KB5G. Specifically, we deliberate on algorithmic innovation and artificial intelligence (AI) in KB5G. We invoke a continuous model-free deep reinforcement learning (DRL) method to minimize energy consumption and virtual network function (VNF) instantiation cost. We present a novel Actor-Critic-based NS approach to stabilize learning called, twin-delayed double-Q soft Actor-Critic (TDSAC) method. The TDSAC enables central unit (CU) to learn continuously to accumulate the knowledge learned in the past to minimize future NS costs. Finally, we present numerical results to showcase the gain of the adopted approach and verify the performance in terms of energy consumption, CPU utilization, and time efficiency.


This work has been supported in part by the research projects 5GSTEPFWD (722429), MonB5G (871780), 5G-SOLUTIONS (856691), AGAUR(2017-SGR-891) and SPOT5G (TEC2017-87456-P).



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MonB5G – Distributed management of Network Slices in beyond 5G 871780
European Commission
5G-SOLUTIONS – 5G Solutions for European Citizens 856691
European Commission