Channel estimation and symbol demodulation for OFDM systems over rapidly varying multipath channels with hybrid deep neural networks
We consider orthogonal frequency division multiplexing over rapidly time-varying multipath
channels, for which performance of standard channel estimation and equalization …
channels, for which performance of standard channel estimation and equalization …
Deep learning assisted channel estimation refinement in uplink OFDM systems under time-varying channels
R Yao, Q Qin, S Wang, N Qi, Y Fan… - … and Mobile Computing …, 2021 - ieeexplore.ieee.org
In various practical orthogonal frequency-division multiplexing (OFDM) systems, the
estimation accuracy at the receiver is challenging, and, specifically when operate over time …
estimation accuracy at the receiver is challenging, and, specifically when operate over time …
A novel OFDM equalizer for large doppler shift channel through deep learning
Q Huang, C Zhao, M Jiang, X Li… - 2019 IEEE 90th Vehicular …, 2019 - ieeexplore.ieee.org
In this paper, we propose a practical deep neural network for OFDM symbol equalization
and demonstrate its advantages in combating large Doppler Shift. In particular, a novel zero …
and demonstrate its advantages in combating large Doppler Shift. In particular, a novel zero …
Power of deep learning for channel estimation and signal detection in OFDM systems
This letter presents our initial results in deep learning for channel estimation and signal
detection in orthogonal frequency-division multiplexing (OFDM) systems. In this letter, we …
detection in orthogonal frequency-division multiplexing (OFDM) systems. In this letter, we …
Deep learning based channel estimation for OFDM systems with doubly selective channel
Q Peng, J Li, H Shi - IEEE Communications Letters, 2022 - ieeexplore.ieee.org
In wireless communication orthogonal frequency division multiplexing (OFDM) systems with
high mobility, channel estimation becomes challenging due to the double selectivity of …
high mobility, channel estimation becomes challenging due to the double selectivity of …
Deep learning-assisted OFDM channel estimation and signal detection technology
J Li, Z Zhang, Y Wang, B He… - IEEE Communications …, 2023 - ieeexplore.ieee.org
The orthogonal frequency division multiplexing (OFDM) technique has received wide
attention because of its high spectrum utilization. However, the drawback of inter-subcarrier …
attention because of its high spectrum utilization. However, the drawback of inter-subcarrier …
RecNet: Deep learning-based OFDM receiver with semi-blind channel estimation
C Liu, T Arslan - … IEEE International Symposium on Circuits and …, 2020 - ieeexplore.ieee.org
This paper proposes a novel deep learning-based system for channel estimation and signal
detection in an orthogonal frequency-division multiplexing (OFDM) system. Different from …
detection in an orthogonal frequency-division multiplexing (OFDM) system. Different from …
Denoising generalization performance of channel estimation in multipath time-varying OFDM systems
Y Li, X Bian, M Li - Sensors, 2023 - mdpi.com
Although Orthogonal Frequency Division Multiplexing (OFDM) technology is still the key
transmission waveform technology in 5G, traditional channel estimation algorithms are no …
transmission waveform technology in 5G, traditional channel estimation algorithms are no …
RoemNet: Robust meta learning based channel estimation in OFDM systems
H Mao, H Lu, Y Lu, D Zhu - ICC 2019-2019 IEEE International …, 2019 - ieeexplore.ieee.org
Recently, in order to achieve performance improvement in scenarios where the channel is
either unknown, or too complex for an analytical description, Neural Network (NN) based …
either unknown, or too complex for an analytical description, Neural Network (NN) based …
CVNN-based Channel Estimation and Equalization in OFDM Systems Without Cyclic Prefix
In modern communication systems operating with Orthogonal Frequency-Division
Multiplexing (OFDM), channel estimation requires minimal complexity with one-tap …
Multiplexing (OFDM), channel estimation requires minimal complexity with one-tap …