scholarly journals Pilot-Assisted Channel Estimation and Signal Detection in Uplink Multi-User MIMO Systems With Deep Learning

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 44936-44946
Author(s):  
Xiaoming Wang ◽  
Hang Hua ◽  
Youyun Xu
Keyword(s):  
Deep Learning ◽  
Channel Estimation ◽  
Signal Detection ◽  
Mimo Systems

Deep Learning-Based Beamspace Channel Estimation in mmWave Massive MIMO Systems

2020 ◽  
Vol 9 (12) ◽  
pp. 2212-2215
Author(s):  
Yinghui Zhang ◽  
Yifan Mu ◽  
Yang Liu ◽  
Tiankui Zhang ◽  
Yi Qian
Keyword(s):  
Deep Learning ◽  
Channel Estimation ◽  
Massive Mimo ◽  
Mimo Systems

scholarly journals Bi-LSTM based deep learning method for 5G signal detection and channel estimation

2021 ◽  
Vol 5 (4) ◽  
pp. 334-341
Author(s):  
D Venkata Ratnam ◽  
◽  
K Nageswara Rao ◽  
Keyword(s):  
Deep Learning ◽  
Wireless Communication ◽  
Channel Estimation ◽  
Signal Detection ◽  
Communication Systems ◽  
Estimation Method ◽  
Estimation Methods ◽  
Delay Spread ◽  
Learning Method ◽  
The Impact

<abstract> <p>The advanced neural network methods solve significant signal estimation and channel characterization difficulties in the next-generation 5G wireless communication systems. The number of transmitted signal copies received through multiple paths at the receiver leads to delay spread, which intern causes interference in communication. These adverse effects of the interference can be mitigated with the orthogonal frequency division modulation (OFDM) technique. Furthermore, the proper signal detection methods optimal channel estimation enhances the performance of the multicarrier wireless communication system. In this paper, bi-directional long short-term memory (Bi-LSTM) based deep learning method is implemented to estimate the channel in different multipath scenarios. The impact of the pilots and cyclic prefix on the performance of Bi LSTM algorithm is analyzed. It is evident from the symbol-error rate (SER) results that the Bi-LSTM algorithm performs better than the state of art channel estimation methods known as the Minimum Mean Square and Error (MMSE) estimation method.</p> </abstract>

scholarly journals Deep Learning-Based Channel Estimation for Beamspace mmWave Massive MIMO Systems

2018 ◽  
Vol 7 (5) ◽  
pp. 852-855 ◽  
Author(s):  
Hengtao He ◽  
Chao-Kai Wen ◽  
Shi Jin ◽  
Geoffrey Ye Li
Keyword(s):  
Deep Learning ◽  
Channel Estimation ◽  
Massive Mimo ◽  
Mimo Systems
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