Adaptive blind equalization for MIMO systems uadaptive blind equalization for MIMO systems under α-stable noise environmentnder α-stable noise environment

Li, Sen; Qiu, Tianshuang; Zha, Daifeng

doi:10.1109/lcomm.2009.081982

Cited by 22 publications

(4 citation statements)

References 9 publications

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“…Due to the characteristic of alpha‐stable distribution, the conventional SNR becomes meaningless. Therefore, generalized SNR (GSNR) [31] is defined in impulsive noise environment, the expression is:

G S N R = 10 lg \frac{σ_{s}^{2}}{γ}

where

σ_{s}^{2}

is the variance of the input signal, γis the dispersion parameter of alpha‐stable distribution.…”

Section: Alpha‐stable Distributionmentioning

confidence: 99%

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Adaptive blind equalization of fast time‐varying channel with frequency estimation in impulsive noise environment

et al. 2021

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In this paper, a novel source signal recovery method for fast time-varying channels described by complex exponential-basis expansion model (CE-BEM) in the impulsive noise environment is proposed. This method consists of two phases. The first phase is the equalization of fast time-varying channels, in this phase, a novel algorithm FSE-FLOS-CMA is proposed. The convergence performance of this newly proposed algorithm is much better than that of existing fractionally spaced equalizer-constant modulus algorithm (FSE-CMA) in impulsive noise environment. In the second phase, a novel frequency estimation method is proposed. The estimated frequency in impulsive noise environment is obtained by calculating a specific p-order fractional low-order cyclic moment of the equalized signal. Simulation results show that the proposed FSE-FLOS-CMA and frequency estimation method can effectively estimate the source signals transmitted through the fast time-varying channels in impulsive noise environment.

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G S N R = 10 lg \frac{σ_{s}^{2}}{γ}

where

σ_{s}^{2}

is the variance of the input signal, γis the dispersion parameter of alpha‐stable distribution.…”

Section: Alpha‐stable Distributionmentioning

confidence: 99%

“…Due to the characteristic of alpha-stable distribution, the conventional SNR becomes meaningless. Therefore, generalized SNR (GSNR) [31] is defined in impulsive noise environment, the expression is:…”

Section: Alpha-stable Distributionmentioning

confidence: 99%

Adaptive blind equalization of fast time‐varying channel with frequency estimation in impulsive noise environment

et al. 2021

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“…In 5G technology, massive MU-MIMO is considered as a key, as it enhances the spectrum efficiency and energy efficiency in large-scale antenna array (Abou Saleh and Hamouda, 2009). On the other hand, massive MIMO also suffers from some stressful problems in baseband signal processing as antennas are huge (Sen et al , 2009; Liu et al , 2012). So, different algorithms are being used to solve the problem that occurs during the detection of MIMO signals (Sen et al , 2009; Lee and Kim, 2009).…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, massive MIMO also suffers from some stressful problems in baseband signal processing as antennas are huge (Sen et al , 2009; Liu et al , 2012). So, different algorithms are being used to solve the problem that occurs during the detection of MIMO signals (Sen et al , 2009; Lee and Kim, 2009). Initially, based on the posteriori probability, the linear algorithm was used to perform and lower complexity than the traditional ML algorithm (Wan and Chen, 2012; Koc and Le-Ngoc, 2021; Zhang et al , 2021).…”

Section: Introductionmentioning

confidence: 99%

Review on various detectors in massive MIMO technology: a performance analysis

Manju

Ganesh

2021

IJPCC

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Purpose Multiple-input multiple-output (MIMO) combined with multi-user massive MIMO has been a well-known approach for high spectral efficiency in wideband systems, and it was targeted to detect the MIMO signals. The increasing data rates with multiple antennas and multiple users that share the communication channel simultaneously lead to higher capacity requirements and increased complexity. Thus, different detection algorithms were developed for the Massive MIMO. Design/methodology/approach This paper focuses on the various literature analyzes on various detection algorithms and techniques for MIMO detectors. Here, it reviews several research papers and exhibits the significance of each detection method. Findings This paper provides the details of the performance analysis of the MIMO detectors and reveals the best value in the case of each performance measure. Finally, it widens the research issues that can be useful for future researchers to be accomplished in MIMO massive detectors Originality/value This paper has presented a detailed review of the detection of massive MIMO on different algorithms and techniques. The survey mainly focuses on different types of channels used in MIMO detections, the number of antennas used in transmitting signals from the source to destination, and vice-versa. The performance measures and the best performance of each of the detectors are described.

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A Robust Maximum Likelihood Algorithm for Blind Equalization of Communication Systems Impaired by Impulsive Noise

Feng

et al. 2018

Circuits Syst Signal Process

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Adaptive blind equalization for MIMO systems uadaptive blind equalization for MIMO systems under α-stable noise environmentnder α-stable noise environment

Cited by 22 publications

References 9 publications

Adaptive blind equalization of fast time‐varying channel with frequency estimation in impulsive noise environment

Adaptive blind equalization of fast time‐varying channel with frequency estimation in impulsive noise environment

Review on various detectors in massive MIMO technology: a performance analysis

A Robust Maximum Likelihood Algorithm for Blind Equalization of Communication Systems Impaired by Impulsive Noise

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