Performance Comparison of Blind Estimation Algorithms for Spreading Sequence in DSSS Signals

2020 ◽  
Vol 45 (3) ◽  
pp. 593-601
Author(s):  
Cheolsun Park ◽  
Chiho Lee ◽  
Unseop Jeong
Keyword(s):  
Performance Comparison ◽  
Blind Estimation ◽  
Estimation Algorithms ◽  
Spreading Sequence

scholarly journals IpDFT-Tuned Estimation Algorithms for PMUs: Overview and Performance Comparison

2021 ◽  
Vol 11 (5) ◽  
pp. 2318
Author(s):  
David Macii ◽  
Daniel Belega ◽  
Dario Petri
Keyword(s):  
Weighted Least Squares ◽  
Performance Comparison ◽  
Rate Of Change ◽  
Parameters Estimation ◽  
Operating Conditions ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Estimation Algorithms ◽  
Class Testing ◽  
Low Pass

The Interpolated Discrete Fourier Transform (IpDFT) is one of the most popular algorithms for Phasor Measurement Units (PMUs), due to its quite low computational complexity and its good accuracy in various operating conditions. However, the basic IpDFT algorithm can be used also as a preliminary estimator of the amplitude, phase, frequency and rate of change of frequency of voltage or current AC waveforms at times synchronized to the Universal Coordinated Time (UTC). Indeed, another cascaded algorithm can be used to refine the waveform parameters estimation. In this context, the main novelty of this work is a fair and extensive performance comparison of three different state-of-the-art IpDFT-tuned estimation algorithms for PMUs. The three algorithms are: (i) the so-called corrected IpDFT (IpDFTc), which is conceived to compensate for the effect of both the image of the fundamental tone and second-order harmonic; (ii) a frequency-tuned version of the Taylor Weighted Least-Squares (TWLS) algorithm, and (iii) the frequency Down-Conversion and low-pass Filtering (DCF) technique described also in the IEEE/IEC Standard 60255-118-1:2018. The simulation results obtained in the P Class and M Class testing conditions specified in the same Standard show that the IpDFTc algorithm is generally preferable under the effect of steady-state disturbances. On the contrary, the tuned TWLS estimator is usually the best solution when dynamic changes of amplitude, phase or frequency occur. In transient conditions (i.e., under the effect of amplitude or phase steps), the IpDFTc and the tuned TWLS algorithms do not clearly outperform one another. The DCF approach generally returns the worst results. However, its actual performances heavily depend on the adopted low-pass filter.

scholarly journals A Comprehensive Review on Various Estimation Techniques for Multi Input Multi Output Channel

2019 ◽  
Vol 27 (1) ◽  
pp. 262-274 ◽  
Author(s):  
Awwab Qasim Jumaah Althahab ◽  
Sameer Abdul Kadhim Alrufaiaat
Keyword(s):  
Channel Estimation ◽  
Communication Systems ◽  
Multiple Input Multiple Output ◽  
Wireless Channel ◽  
Mimo Channel ◽  
Estimation Accuracy ◽  
Blind Estimation ◽  
Quality Of Data ◽  
Single Input Single Output ◽  
Estimation Algorithms

The problem of wireless channel estimation has been evolving due to some undesirable effects of channel physical properties on transmitted signals. At the receiver end, distortions, delays, attenuations, interferences, and phase shifts are the most issues encounter together with the received signals. In order to overcome channel effects and provide almost a perfect quality of data transmission, channel parameter estimation is needed. In Multiple Input-Multiple Output systems (MIMO), channel estimation is a more complicated step as compared with the Single Input-Single Output systems, SISO, because of the fact that the number of sub-channels that needs estimate is much greater than SISO systems. The fundamental objective of this research paper is to go over the famous and efficient algorithms that have been innovated to solve the problem of MIMO channel estimation in wireless communication systems. In this paper, these techniques have been classified into three groups: non-blind, semi-blind and blind estimation. For each group, a brief illustration is presented for familiar estimation algorithms. Finally, we compare between these techniques based on computational complexity, latency and estimation accuracy.

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