scholarly journals Deep Learning-Based Wireless Channel Estima-tion for MIMO Uncoded Space-Time Labelling Diversity

IEEE Access ◽  
2020 ◽  
pp. 1-1
Author(s):  
Bhekisizwe Mthethwa ◽  
Hongjun Xu
Keyword(s):  
Deep Learning ◽  
Space Time ◽  
Wireless Channel

scholarly journals Wireless Channel Scene Recognition Method Based on an Autocorrelation Function and Deep Learning

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 226324-226336
Author(s):  
Shuguang Ning ◽  
Yigang He ◽  
Lifen Yuan ◽  
Yuan Huang ◽  
Shudong Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Autocorrelation Function ◽  
Wireless Channel ◽  
Scene Recognition ◽  
Recognition Method

scholarly journals DESIGN & IMPLEMENTATION OF POLYPHASE FILTER TO DENOISE OFDM SIGNALS

2021 ◽  
Vol 10 (6) ◽  
pp. 83-89
Author(s):  
GOUSIA NABI DAR ◽  
RAJAT JOSHI
Keyword(s):  
Error Rate ◽  
Orthogonal Frequency Division Multiplexing ◽  
Research Work ◽  
Space Time ◽  
Wireless Channel ◽  
Frequency Division ◽  
Ofdm Systems ◽  
Trellis Codes ◽  
Transit System ◽  
Polyphase Filter

An amazingly effective digital transit system that carries multiple carriers that meet in conjunction with each other over a period of time is known as the Orthogonal Frequency Division Multiplexing (OFDM) system. Among the traditional symbols, there is a need to include a group of guards. However, in OFDM systems this is not required. Although there is an overlap of side bands from each carrier, no interference is involved within the signals found here as they are orthogonal in relation to each other. This research work is based on a wireless channel to reduce the rate of error using space-time trellis codes. In this research project, the minimum error rate is reduced over wireless channels using space time codes and poly-phase filters. The proposed modular simulation is performed at MATLAB and the results show that the minimum bit error rate decreases in the network.

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