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.
翻译：为提高覆盖范围和传输可靠性,窄带信息互联网(NB-IoT)采用的重复做法可以多次重复传输,然而,这导致在信号强度高的情况下浪费无线电资源;此外,在信号质量低的情况下,选择高调和编码办法(MCS)导致网络中大量丢失数据包;此外,需要动态地选择每个用户的有形资源区块数目,这样就可以在用户基础上改进无线电资源的利用;因此,在NB-IoT系统中,动态地调整重复、MCS和无线电资源,称为自动链接配置,至关重要;因此,在本文件中,我们提议SmartCon,这是基于Generative Adversarial 网络(GAN)的深度学习方法,用于在上链或下链列表期间进行自动链接配置,这样,在NB-IoT网络中将大量减少数据包损失率;因此,为了GAN的训练目的,我们使用多Armed Bandrition、MCS和无线电资源,称为自动链接配置。因此,我们提议Smart-Armed Band(MAB)的模拟模型模型测试系统,通过智能测试其升级的运行升级升级的性产出测试机制,通过其模拟升级升级升级升级升级升级的性测试改进其性测试测试测试测试性能能的性能的性能测试性能的性能测试性能能能改进性能能能的性能能测试性能能能能的性能的性能测试性能的性能的性能的性能测试性能的性能能能测试性能测试性能能能能能能能能能机制。
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