Towards Ultra-Reliable 6G in-X Subnetworks: Dynamic Link Adaptation by Deep Reinforcement Learning

TL;DR

This paper introduces a deep reinforcement learning-based link adaptation framework for 6G subnetworks, explicitly targeting the reduction of consecutive packet outages to ensure ultra-reliable low-latency communication in industrial environments.

Contribution

It presents a novel SAC-based DRL method that jointly optimizes energy efficiency and reliability, explicitly reducing outage bursts under dynamic channel conditions.

Findings

Significantly reduces outage bursts compared to baseline algorithms.

Consumes only 18% of the transmission cost of maximum resource policies.

Supports flexible trade-offs between energy efficiency and reliability.

Abstract

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.