标题： 面向6G的X子网络中高可靠性的动态链路适应：通过深度强化学习 显示英文标题

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

摘要： 6G网络由子网络组成，这些子网络预计能够满足工业控制和自动化等关键任务应用的超可靠低延迟通信（URLLC）要求。 在URLLC中经常被忽视的一个方面是连续数据包中断，这可能导致控制回路不稳定，并影响工厂环境中的安全性。 因此，当前的工作提出了一种链路自适应框架，使用基于软演员-评论家（SAC）的深度强化学习（DRL）算法，在动态信道和干扰条件下联合优化能效（EE）和可靠性，以支持极端可靠性要求。 与之前专注于平均可靠性的研究不同，我们的方法通过仅基于观测到的信噪比加干扰比（SINR）的发射功率和码块长度的自适应控制，明确旨在减少突发/连续中断。 在有限码长和服务质量约束下制定联合优化问题，平衡可靠性和EE。 仿真结果表明，所提出的方法显著优于基线算法，在所评估的场景中，减少中断突发的同时，仅消耗全/最大资源分配策略所需传输成本的18%。 该框架通过调整奖励权重，还支持在EE和可靠性之间进行灵活的权衡调整，使其能够适应各种工业需求。 显示英文摘要

摘要： 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.