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Solving Sparse Linear Inverse Problems in Communication Systems: A Deep
  Learning Approach With Adaptive Depth

Solving Sparse Linear Inverse Problems in Communication Systems: A Deep Learning Approach With Adaptive Depth

29 October 2020
Wei Chen
Bowen Zhang
Shimei Jin
B. Ai
Z. Zhong
ArXivPDFHTML

Papers citing "Solving Sparse Linear Inverse Problems in Communication Systems: A Deep Learning Approach With Adaptive Depth"

4 / 4 papers shown
Title
Deep-Unfolding for Next-Generation Transceivers
Deep-Unfolding for Next-Generation Transceivers
Qiyu Hu
Yunlong Cai
Guangyi Zhang
Guanding Yu
Geoffrey Ye Li
25
4
0
15 May 2023
Coordinated Pilot Transmissions for Detecting the Signal Sparsity Level
  in a Massive IoT Network under Rayleigh Fading
Coordinated Pilot Transmissions for Detecting the Signal Sparsity Level in a Massive IoT Network under Rayleigh Fading
Onel L. A. López
Glauber Brante
R. Souza
M. Juntti
Matti Latva-aho
17
4
0
01 May 2022
DDPG-Driven Deep-Unfolding with Adaptive Depth for Channel Estimation
  with Sparse Bayesian Learning
DDPG-Driven Deep-Unfolding with Adaptive Depth for Channel Estimation with Sparse Bayesian Learning
Qiyu Hu
Shuhan Shi
Yunlong Cai
Guanding Yu
BDL
13
13
0
20 Jan 2022
Deep Learning Methods for Solving Linear Inverse Problems: Research
  Directions and Paradigms
Deep Learning Methods for Solving Linear Inverse Problems: Research Directions and Paradigms
Yanna Bai
Wei Chen
Jie Chen
Weisi Guo
40
66
0
27 Jul 2020
1