论文标题
物理层的深度学习：系统对5G及以后的挑战和应用
Deep Learning at the Physical Layer: System Challenges and Applications to 5G and Beyond
论文作者
论文摘要
物联网（IoT）的空前要求（IoT）使频谱资源的细粒度优化成为迫切的必要性。因此，设计能够实时从频谱中提取知识并相应选择最佳频谱访问策略的技术变得比以往任何时候都变得更加重要。此外，5G及以后（5GB）网络将需要复杂的管理方案来处理自适应束管理和速率选择等问题。尽管深度学习（DL）在建模复杂现象方面取得了成功，但商业上可用的无线设备仍然远离实际采用基于学习的技术来优化其频谱使用情况。在本文中，我们首先讨论在物理层上实时DL的必要性，然后总结艺术的当前状态和现有限制。我们通过讨论研究挑战的议程以及如何应用DL来解决5GB网络中的关键问题来结束本文。
The unprecedented requirements of the Internet of Things (IoT) have made fine-grained optimization of spectrum resources an urgent necessity. Thus, designing techniques able to extract knowledge from the spectrum in real time and select the optimal spectrum access strategy accordingly has become more important than ever. Moreover, 5G and beyond (5GB) networks will require complex management schemes to deal with problems such as adaptive beam management and rate selection. Although deep learning (DL) has been successful in modeling complex phenomena, commercially-available wireless devices are still very far from actually adopting learning-based techniques to optimize their spectrum usage. In this paper, we first discuss the need for real-time DL at the physical layer, and then summarize the current state of the art and existing limitations. We conclude the paper by discussing an agenda of research challenges and how DL can be applied to address crucial problems in 5GB networks.
