论文标题
无线通信中的频道类型识别：一种深度学习方法
Channel Type Recognition in Wireless Communications: A Deep Learning Approach
论文作者
论文摘要
在本文中，我们提出了两种新颖且实用的深度学习算法来解决无线通道类型（WCT）识别问题。具体而言，WCT识别问题是重新出现的，因为它们的相似性是深度学习中的一个分类问题，在该问题中，深度神经网络（DNN）经过了离线训练，其典型的WCT具有典型的第五代（5G）和5G无线通信，然后将其用于在线WCT确定。在第一个算法中，一个WCT被视为一个任务。虽然在第二个方案中，一个WCT的共同特征是几个独立特征，每个特征都被视为一项任务，并通过以多任务学习方式训练DNN分别对DNN进行了分类，并且最终的WCT通过这些通道特征的组合确定。仿真结果表明，所提出的算法可以以高精度即时对各种WCT进行分类，从而导致令人满意的块错误率和吞吐量，并且表现优于代表性的基线WCT确定方案。
In this paper, we propose two novel and practical deep-learning-based algorithms to solve the wireless channel type (WCT) recognition problem. Specifically, the WCT recognition problem is recast as a classification problem in deep learning due to their similarities, where a deep neural network (DNN) is trained off-line with a diversity of typical WCTs for fifth-generation (5G) and beyond-5G wireless communications, which is then utilized to perform online WCT determination. In the first algorithm, one WCT is regarded as a single task. While in the second scheme, one WCT is jointly characterized by several independent features, each of which is treated as a task and is classified respectively by training a DNN in a multi-task-learning manner, and the final WCT is identified by the combination of those channel features. Simulation results show that the proposed algorithms can classify various WCTs instantaneously with high accuracy, result in satisfactory block error rate and throughput, and outperform a representative baseline WCT determination scheme.
