SmartCon：面向5G和B5G的窄带物联网中基于深度概率学习的智能链接配置

为了提高覆盖率和传输可靠性，窄带物联网（NB-IoT）采用的重复传输方式允许多次重复传输。然而，当信号强度较高时，这就造成了无线电资源的浪费。此外，在低信号质量下，选择更高的调制和编码方案（MCS）水平会导致网络中巨大的数据包损失。此外，每个用户的物理资源块（PRB）的数量需要动态选择，这样可以在每个用户的基础上提高无线电资源的利用率。因此，在NB-IoT系统中，重复次数、MCS和无线电资源的动态适应，即自动链路配置，是至关重要的。因此，在本文中，我们提出了SmartCon，这是一种基于生成对抗网络（GAN）的深度学习方法，用于上行或下行调度期间的自动链路配置，从而使NB-IoT网络的丢包率大大降低。为了训练GAN，我们使用了基于多臂匪徒（MAB）的强化学习机制，根据当前的网络状况智能地调整其输出。通过模拟对SmartCon的性能进行了彻底的评估，与基线方案相比，SmartCon明显改善了NB-IoT系统的性能。

原文题目：SmartCon: Deep Probabilistic Learning Based Intelligent Link-Configuration in Narrowband-IoT Towards 5G and B5G

原文：To enhance the coverage and transmission reliability, repetitions adopted by Narrowband Internet of Things (NB-IoT) allow repeating transmissions several times. However, this results in a waste of radio resources when the signal strength is high. In addition, in low signal quality, the selection of a higher modulation and coding scheme (MCS) level leads to a huge packet loss in the network. Moreover, the number of physical resource blocks (PRBs) per-user needs to be chosen dynamically, such that the utilization of radio resources can be improved on per-user basis. Therefore, in NB-IoT systems, dynamic adaptation of repetitions, MCS, and radio resources, known as auto link-configuration, is crucial. Accordingly, in this paper, we propose SmartCon which is a Generative Adversarial Network (GAN)-based deep learning approach for auto link-configuration during uplink or downlink scheduling, such that the packet loss rate is significantly reduced in NB-IoT networks. For the training purpose of the GAN, we use a Multi-Armed Bandit (MAB)-based reinforcement learning mechanism that intelligently tunes its output depending on the present network condition. The performance of SmartCon is thoroughly evaluated through simulations where it is shown to significantly improve the performance of NB-IoT systems compared to baseline schemes.