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
2020 ◽  
Vol 9 (12) ◽  
pp. 2212-2215
Author(s):  
Yinghui Zhang ◽  
Yifan Mu ◽  
Yang Liu ◽  
Tiankui Zhang ◽  
Yi Qian

2021 ◽  
Vol 5 (4) ◽  
pp. 334-341
Author(s):  
D Venkata Ratnam ◽  
◽  
K Nageswara Rao ◽  

<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>


2018 ◽  
Vol 7 (5) ◽  
pp. 852-855 ◽  
Author(s):  
Hengtao He ◽  
Chao-Kai Wen ◽  
Shi Jin ◽  
Geoffrey Ye Li

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