Channel Estimation and User Identification with Deep Learning for Massive Machine-Type Communications

2021 ◽  
pp. 1-1
Author(s):  
Bryan Liu ◽  
Zhiqiang Wei ◽  
Weijie Yuan ◽  
Jinhong Yuan ◽  
Milutin Pajovic
Keyword(s):  
Deep Learning ◽  
Channel Estimation ◽  
User Identification ◽  
Machine Type ◽  
Machine Type Communications

scholarly journals Channel Estimation Techniques for OFDM and UFMC Systems for 5G Communications

2019 ◽  
Vol 8 (2S8) ◽  
pp. 1776-1778
Keyword(s):  
Channel Estimation ◽  
Communication Systems ◽  
Orthogonal Frequency Division Multiplexing ◽  
Wireless Communication Systems ◽  
Modulation Scheme ◽  
Frequency Division ◽  
Ofdm Systems ◽  
Machine Type ◽  
Machine Type Communications ◽  
Simulation Results

In this paper, pilot-assisted techniques for channel estimation (CE) are simulated for Universal Filtered Multi-Carrier (UFMC) modulation scheme. UFMC aims at replacing orthogonal frequency division multiplexing (OFDM) and improves performance and robustness in the case of timefrequency misalignment. These techniques efficiently support Internet of Things (IoT) and massive machine type communications (mMTC), which are identified as challenges for 5G wireless communication systems (WCS). Pilot-aided techniques are adopted and applied to OFDM and UFMC. Simulation results are supplemented to compare the performance of UFMC systems with conventional CP-OFDM systems.

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