论文标题
朝着深度学习辅助的无线渠道估计和渠道状态信息反馈6G
Towards Deep Learning-aided Wireless Channel Estimation and Channel State Information Feedback for 6G
论文作者
论文摘要
深度学习（DL）是人工智能（AI）技术的一个分支，在图像分类和细分，语音识别，语言翻译等各种学科中表现出了巨大的希望。近年来，DL的这一显着成功激发了人们对将此范式应用于无线通道估计的日益增长的兴趣。由于DL原理本质上是归纳性的，并且与常规的基于规则的算法不同，因此当人们尝试将DL技术用于频道估计时，因此很容易被如此多的旋钮控制和混淆，以控制和小细节要注意。本文的主要目的是讨论基于DL的无线通道估计和渠道状态信息（CSI）的关键问题和可能的解决方案，包括DL模型选择，培训数据获取和6G的神经网络设计。具体而言，我们提供了几个案例研究以及数值实验，以证明基于DL的无线通道估计框架的有效性。
Deep learning (DL), a branch of artificial intelligence (AI) techniques, has shown great promise in various disciplines such as image classification and segmentation, speech recognition, language translation, among others. This remarkable success of DL has stimulated increasing interest in applying this paradigm to wireless channel estimation in recent years. Since DL principles are inductive in nature and distinct from the conventional rule-based algorithms, when one tries to use DL technique to the channel estimation, one might easily get stuck and confused by so many knobs to control and small details to be aware of. The primary purpose of this paper is to discuss key issues and possible solutions in DL-based wireless channel estimation and channel state information (CSI) feedback including the DL model selection, training data acquisition, and neural network design for 6G. Specifically, we present several case studies together with the numerical experiments to demonstrate the effectiveness of the DL-based wireless channel estimation framework.
