论文标题
免费访问：用于检测短包的机器学习
Grant-Free Access: Machine Learning for Detection of Short Packets
论文作者
论文摘要
在本文中，我们探讨了机器学习方法作为执行数据包检测的相关性的有效替代方法。我们针对基于卫星的大型机器类型通信和物联网场景，我们的重点是通过完全异步的Aloha协议在大量终端之间共享的公共通道上，以尝试交付短数据包。在此设置中，我们测试了两种算法，神经网络和随机森林的性能，这些算法可提供对{传统}技术的实质性改进。在用户传输之间存在碰撞的情况下，在检测和虚假警报概率方面证明了出色的性能。还研究了机器学习从传入信号中提取更多信息的能力，从而讨论了根据他们经历的干扰水平对检测到的前序进行分类的可能性。
In this paper, we explore the use of machine learning methods as an efficient alternative to correlation in performing packet detection. Targeting satellite-based massive machine type communications and internet of things scenarios, our focus is on a common channel shared among a large number of terminals via a fully asynchronous ALOHA protocol to attempt delivery of short data packets. In this setup, we test the performance of two algorithms, neural networks and random forest, which are shown to provide substantial improvements over {traditional} techniques. Excellent performance is demonstrated in terms of detection and false alarm probability also in the presence of collisions among user transmissions. The ability of machine learning to extract further information from incoming signals is also studied, discussing the possibility to classify detected preambles based on the level of interference they undergo.
