Gigabit Mobile Communications Using Real- Time MIMO-OFDM Signal Processing

2006 ◽  
pp. 315-352 ◽  
Author(s):  
Volker Jungnickel ◽  
Andreas Forck ◽  
Thomas Haustein ◽  
Christoph Juchems ◽  
Wolfgang Zirwas
Keyword(s):  
Signal Processing ◽  
Real Time ◽  
Mobile Communications ◽  
Ofdm Signal ◽  
Mimo Ofdm

Gigabit Mobile Communications Using Real-Time MIMO-OFDM Signal Processing

2018 ◽  
pp. 315-352
Author(s):  
Volker Jungnickel ◽  
Andreas Forck ◽  
Thomas Haustein ◽  
Christoph Juchems ◽  
Wolfgang Zirwas
Keyword(s):  
Signal Processing ◽  
Real Time ◽  
Mobile Communications ◽  
Ofdm Signal ◽  
Mimo Ofdm

Multi-User Chaos MIMO-OFDM Scheme for Secure Mobile Communications

2014 ◽  
Vol 2 ◽  
pp. 264-268
Author(s):  
Yuma Inaba ◽  
Eiji Okamoto
Keyword(s):  
Mobile Communications ◽  
Mimo Ofdm

Research on Real-Time Signal Processing of the Missile-Borne SAR

2011 ◽  
Vol 30 (4) ◽  
pp. 1010-1013
Author(s):  
Zhi-ming He ◽  
Jiang Zhu ◽  
Bo Zhou
Keyword(s):  
Signal Processing ◽  
Real Time ◽  
Time Signal ◽  
Real Time Signal Processing

Real-time signal processing in field programmable gate array based digital gamma-ray spectrometer

2020 ◽  
Vol 91 (10) ◽  
pp. 104707
Author(s):  
Yinyu Liu ◽  
Hao Xiong ◽  
Chunhui Dong ◽  
Chaoyang Zhao ◽  
Quanfeng Zhou ◽  
...  
Keyword(s):  
Signal Processing ◽  
Real Time ◽  
Field Programmable Gate Array ◽  
Gamma Ray ◽  
Time Signal ◽  
Gamma Ray Spectrometer ◽  
Field Programmable ◽  
Gate Array ◽  
Real Time Signal Processing

A New Method for Phase Difference Estimation Based on Time-Varying Signal Model

2013 ◽  
Vol 333-335 ◽  
pp. 650-655
Author(s):  
Peng Hui Niu ◽  
Yin Lei Qin ◽  
Shun Ping Qu ◽  
Yang Lou
Keyword(s):  
Signal Processing ◽  
Real Time ◽  
Phase Difference ◽  
Time Signal ◽  
Time Interval ◽  
Time Varying ◽  
Signal Model ◽  
Time Frequency ◽  
Mass Flowmeter ◽  
Coriolis Mass Flowmeter

A new signal processing method for phase difference estimation was proposed based on time-varying signal model, whose frequency, amplitude and phase are time-varying. And then be applied Coriolis mass flowmeter signal. First, a bandpass filtering FIR filter was applied to filter the sensor output signal in order to improve SNR. Then, the signal frequency could be calculated based on short-time frequency estimation. Finally, by short window intercepting, the DTFT algorithm with negative frequency contribution was introduced to calculate the real-time phase difference between two enhanced signals. With the frequency and the phase difference obtained, the time interval of two signals was calculated. Simulation results show that the algorithms studied are efficient. Furthermore, the computation of algorithms studied is simple so that it can be applied to real-time signal processing for Coriolis mass flowmeter.

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