Towards Ultra-Reliable 6G in-X Subnetworks: Dynamic Link Adaptation by Deep Reinforcement Learning
On the map: Deep reinforcement learning methods → RL for energy, networks and operations
Abstract (arXiv)
6G networks are composed of subnetworks expected to meet ultra-reliable low-latency communication (URLLC) requirements for mission-critical applications such as industrial control and automation. An often-ignored aspect in URLLC is consecutive packet outages, which can destabilize control loops and compromise safety in in-factory environments. Hence, the current work proposes a link adaptation framework to support extreme reliability requirements using the soft actor-critic (SAC)-based deep reinforcement learning (DRL) algorithm that jointly optimizes energy efficiency (EE) and reliability under dynamic channel and interference conditions. Unlike prior work focusing on average reliability, our method explicitly targets reducing burst/consecutive outages through adaptive control of transmit power and blocklength based solely on the observed signal-to-interference-plus-noise ratio (SINR). The joint optimization problem is formulated under finite blocklength and quality of service constraints, balancing reliability and EE. Simulation results show that the proposed method significantly outperforms the baseline algorithms, reducing outage bursts while consuming only 18% of the transmission cost required by a full/maximum resource allocation policy in the evaluated scenario. The framework also supports flexible trade-off tuning between EE and reliability by adjusting reward weights, making it adaptable to diverse industrial requirements.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 10 of its 33 references that are in the map. First posted on arXiv 16 Jul 2025.
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10 of 33 references.
References · 10
- 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
- 2022Exploration in Deep Reinforcement Learning: A Surveycited by 166
- 2020Resource Management in Wireless Networks via Multi-Agent Deep Reinforcement Learningcited by 42
- 2022Multi-agent Reinforcement Learning for Dynamic Resource Management in 6G in-X Subnetworkscited by 9
- 2021Deep Reinforcement Learning for Wireless Scheduling in Distributed Networked Controlcited by 5
- 2023Optimization Theory Based Deep Reinforcement Learning for Resource Allocation in Ultra-Reliable Wireless Networked Control Systemscited by 3
- 2024Meta Reinforcement Learning for Resource Allocation in Aerial Active-RIS-assisted Networks with Rate-Splitting Multiple Accesscited by 3
- 2020A Predictive Interference Management Algorithm for URLLC in Beyond 5G Networkscited by 2
- 2023Reliability and Delay Analysis of 3-Dimensional Networks with Multi-Connectivity: Satellite, HAPs, and Cellular Communicationscited by 2
- 2025Ultra-High Reliability by Predictive Interference Management Using Extreme Value Theorycited by 1
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- 2025Adaptive Cooperative Transmission Design for Ultra-Reliable Low-Latency Communications via Deep Reinforcement Learning
- 2025A Hybrid Reinforcement Learning Framework for Hard Latency Constrained Resource Scheduling
- 2026Intelligent Radio Resource Slicing for 6G In-Body Subnetworks
- 2025Optimisation of Resource Allocation in Heterogeneous Wireless Networks Using Deep Reinforcement Learning
- 2024Offline Reinforcement Learning and Sequence Modeling for Downlink Link Adaptation