.png){kind=icon}
返回
Deep Learning for Channel Estimation: Interpretation, Performance, and Comparison
.png){kind=icon}
OA
AI
DOI：10.1109/TWC.2020.3042074.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Deep learning (DL) has emerged as an effective tool for channel estimation in wireless communication systems, especially under some imperfect environments. However, even with such unprecedented success, DL methods are often regarded as black boxes and are lack of explanations on their internal mechanisms, which severely limits their further improvement and extension. In this paper, we present preliminary theoretical analysis on DL based channel estimation for single-input multiple-output (SIMO) systems to understand and interpret its internal mechanisms. As deep neural network (DNN) with rectified linear unit (ReLU) activation function is mathematically equivalent to a piecewise linear function, the corresponding DL estimator can achieve universal approximation to a large family of functions by making efficient use of piecewise linearity. We demonstrate that DL based channel estimation does not restrict to any specific signal model and asymptotically approaches to the minimum mean-squared error (MMSE) estimation in various scenarios without requiring any prior knowledge of channel statistics. Therefore, DL based channel estimation outperforms or is at least comparable with traditional channel estimation, depending on the types of channels. Simulation results confirm the accuracy of the proposed interpretation and demonstrate the effectiveness of DL based channel estimation under both linear and nonlinear signal models.
Keyword:
Channel estimation
Wireless communication
Estimation
Training
SIMO communication
Neurons
Neural networks
Explainable deep learning
input space partition
channel estimation
ReLU
期刊
IF:
10.7
论文数:
1.3W
被引数:
5.3W
机构
引用论文
Real-time and offline techniques for identifying obstructive sleep apnea patients用于识别阻塞性睡眠呼吸暂停患者的实时和离线技术
PREAI
分享收藏Deep Learning-Based Downlink Channel Prediction for FDD Massive MIMO System基于深度学习的FDD大规模MIMO系统下行信道预测
OAAI
OAAI分享收藏Deep-Learning-Based Wireless Resource Allocation With Application to Vehicular Networks基于深度学习的无线资源分配及其在车载网络中的应用.png)
PROCEEDINGS OF THE IEEEOAAI
PROCEEDINGS OF THE IEEEIF25.9
OAAI分享收藏
查看更多内容