Considering grant-free transmissions in low-power IoT networks with unknown time-frequency distribution of interference, we address the problem of Dynamic Resource Configuration (DRC), which amounts to a Markov decision process. Unfortunately, off-the-shelf methods based on single-objective reinforcement learning cannot guarantee energy-efficient transmission, especially when all frequency-domain channels in a time interval are interfered. Therefore, we propose a novel DRC scheme where configuration policies are optimized with a Multi-Objective Reinforcement Learning (MORL) framework. Numerical results show that the average decision error rate achieved by the MORL-based DRC can be even less than 12% of that yielded by the conventional R-learning-based approach.

中文翻译：

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低功耗物联网网络的动态资源配置：一种多目标强化学习方法

考虑到干扰时频分布未知的低功率物联网网络中的无授权传输，我们解决了动态资源配置 (DRC) 的问题，这相当于马尔可夫决策过程。不幸的是，基于单目标强化学习的现成方法无法保证高能效传输，尤其是当一个时间间隔内的所有频域信道都受到干扰时。因此，我们提出了一种新颖的 DRC 方案，其中使用多目标强化学习（MORL）框架优化配置策略。数值结果表明，基于 MORL 的 DRC 实现的平均决策错误率甚至可以低于传统的基于 R 学习的方法产生的决策错误率的 12%。