Understanding Wireless ChannelsThrough NeRF2
Despite Maxwell's formulation of the electromagnetic wave laws over a century and a half
ago, accurately modeling the transmission of RF signals within electrically complex …
ago, accurately modeling the transmission of RF signals within electrically complex …
Toward an intelligent edge: Wireless communication meets machine learning
The recent revival of AI is revolutionizing almost every branch of science and technology.
Given the ubiquitous smart mobile gadgets and IoT devices, it is expected that a majority of …
Given the ubiquitous smart mobile gadgets and IoT devices, it is expected that a majority of …
One-dimensional deep attention convolution network (ODACN) for signals classification
S Yang, C Yang, D Feng, X Hao, M Wang - IEEE Access, 2019 - ieeexplore.ieee.org
Handcraft features are commonly used for signal classification, which is a time-consuming
feature engineering. In order to develop a general and robust feature learning method for …
feature engineering. In order to develop a general and robust feature learning method for …
Improving next-generation wireless network performance and reliability with deep learning
FB Mismar - 2020 - repositories.lib.utexas.edu
A rudimentary question whether machine learning in general, or deep learning in particular,
could add to the well-established field of wireless communications, which has been evolving …
could add to the well-established field of wireless communications, which has been evolving …
[图书][B] Machine learning and wireless communications
How can machine learning help the design of future communication networks-and how can
future networks meet the demands of emerging machine learning applications? Discover the …
future networks meet the demands of emerging machine learning applications? Discover the …
Research on modulation recognition algorithm based on channel and spatial self-attention mechanism
W Zhang, Y Sun, K Xue, A Yao - IEEE Access, 2023 - ieeexplore.ieee.org
In the harsh electromagnetic environment with strong interference, the prior information of
the received signal can not be fully obtained, and considering the complex and variable …
the received signal can not be fully obtained, and considering the complex and variable …
Deep learning at the physical layer: System challenges and applications to 5G and beyond
F Restuccia, T Melodia - IEEE Communications Magazine, 2020 - ieeexplore.ieee.org
The unprecedented requirements of IoT have made fine-grained optimization of spectrum
resources an urgent necessity. Thus, designing techniques able to extract knowledge from …
resources an urgent necessity. Thus, designing techniques able to extract knowledge from …
深度学习辅助的5G OFDM 系统的信道估计.
王义元， 常俊， 卢中奎， 余福慧… - Telecommunication …, 2024 - search.ebscohost.com
传统的信道估计算法难以满足5G 系统中的高速率低时延的需求. 针对该问题,
将通信信道的时频响应视为二维图像, 提出了一种基于图像恢复技术的信道估计方法. 首先 …
将通信信道的时频响应视为二维图像, 提出了一种基于图像恢复技术的信道估计方法. 首先 …
Learning to communicate with autoencoders: Rethinking wireless systems with deep learning
ME Morocho-Cayamcela, JN Njoku… - … in Information and …, 2020 - ieeexplore.ieee.org
The design and implementation of conventional communication systems are based on
strong probabilistic models and assumptions. These fixed and conventional communication …
strong probabilistic models and assumptions. These fixed and conventional communication …
A denoising radio classifier with residual learning for modulation recognition
H Zhu, L Zhou, C Chen - 2021 IEEE 21st International …, 2021 - ieeexplore.ieee.org
Deep learning has great potential in modulation recognition. However, when noise causes
signals distortion, the performance of deep learning algorithms degrades dramatically, and …
signals distortion, the performance of deep learning algorithms degrades dramatically, and …