MIMO Receiver Using Reduced Complexity Sequence Estimation With Channel Estimation and Tracking

Kho, Yau Hee; Taylor, D.P.

doi:10.1109/tvt.2008.926589

Cited by 4 publications

(2 citation statements)

References 18 publications

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“…1 Channel estimation is an important technique used in a wireless mobile network system, where the channel is switched by a particular instant of time. 2 Wireless mobile communication is often influenced by multichannel obstacles like reflection results and elements including infrastructures, mountains, and other obstacles. The channel estimation is varied concerning time and also obtained reliable and more data rates at the receiving end.…”

Section: Introductionmentioning

confidence: 99%

“…Over the past years, Multiuser Multiple‐Input Multiple Output (MIMO) systems have achieved more attention and gained promising solutions for enhancing the spectral efficiency of wireless networks 1 . Channel estimation is an important technique used in a wireless mobile network system, where the channel is switched by a particular instant of time 2 . Wireless mobile communication is often influenced by multichannel obstacles like reflection results and elements including infrastructures, mountains, and other obstacles.…”

Section: Introductionmentioning

confidence: 99%

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Ensemble learning‐based channel estimation and hybrid precoding for millimeter‐wave massive multiple input multiple output system

Reddy

2023

Trans Emerging Tel Tech

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Millimeter‐Wave (mmWave) massive Multi‐User Multiple Input Multiple Output (MU‐MIMO) utilizes the hybrid analog precoding design helps to minimize the amount of Radio‐Frequency (RF) chains without causing loss of sum‐rate performance. The Channel Estimation (CE) is designed to demand the conditions of channels with high time‐varying features. The existing CE frameworks are extremely complex. The accurate CE is still challenging in mmWave massive MIMO system based on the hardware constraints, a maximum number of antennas, and so on. Various proficient algorithms were combined to yield reliable channel estimates. The mmWave massive MIMO system utilizes hybrid precoding to minimize energy consumption loss and reduce hardware complexity. The fully connected layer in hybrid precoding results in high energy efficiency loss. This proposed work aims to develop two ensemble deep learning models for CE and hybrid precoding of the “mmWave massive MIMO system.” Here, the estimation of the channel is performed by a “deep Convolutional Neural Network (CNN) with a Recurrent Neural Network (RNN).” With the reconstructed channel, the hybrid precoding phase is performed by deep CNN with AdaBoost. The performance of CE and hybrid precoding is improved with Self Adaptive Searched Galactic Swarm Optimization (SAS‐GSO) by attaining the maximum spectral efficiency and also minimizing the Normalized Mean‐Squared Error (NMSE). Hence, the simulation result is shown that the proposed model achieves enhanced performance. Throughout the analysis, at the 20th iteration, the SAS‐GSO‐ensemble approach gives a lower NMSE rate, which is 31%, 25.9%, 20%, 19.6%, 19.3%, 18.6%, and 18.3% superior to OMP‐Ensemble, AMP‐Ensemble, DLCS‐Ensemble, DGMP‐Ensemble, QALS‐Ensemble, DLQP‐Ensemble, and CSO‐Ensemble, respectively, for hybrid precoding. Therefore, the offered mmWave massive MIMO is achieved better performance regarding diverse error metrics.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ensemble learning‐based channel estimation and hybrid precoding for millimeter‐wave massive multiple input multiple output system

Reddy

2023

Trans Emerging Tel Tech

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A comprehensive study on channel estimation techniques using machine learning for massive MIMO systems

Madurai,

Sivaraman,

Balasubramaiyan

et al. 2023

SUSTAINABLE DEVELOPMENTS IN MATERIALS SCIENCE, TECHNOLOGY AND ENGINEERING: Sustainable Development in Material Science of Today

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RETRACTED: Channel Estimation of MIMO-OFDM through Wavelet Transform to Improve the Spectral Efficiency and Data Transmission Rate

Yogeeswaran¹,

Swaminathan²,

Manimaran³

2020

IOP Conf. Ser.: Mater. Sci. Eng.

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We define a new wavelet transform that is based on a recently defined family of scaling functions: the fractional B-splines. The interest of this family is that they interpolate between the integer degrees of polynomial B- splines and that they allow a fractional order of approximation. The approximation can determine noise in communication signals and help in signal reconstruction and channel estimation in MIMO receiver system. In free space optical communication links, the turbulent atmospheric channel causes fluctuations in both intensity and phase of the transmitted optical signal which degrades their performance. In order to make free space optical communications commercially viable, there is a need for characterizing the atmospheric effects on the received signal statistics, and possibly correcting for the signal degradation using physical optics principles and advanced real-time signal processing techniques. As part of this work, we have constructed an experimental free space optical link over 100 meters so as to characterize the effects of inner-scale and outer-scale turbulence effects, and used it as the platform for carrying out the above objectives. Through these techniques, we have demonstrated bit error rate (BER) reduction and propose to estimate channel accurately.

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MIMO Receiver Using Reduced Complexity Sequence Estimation With Channel Estimation and Tracking

Cited by 4 publications

References 18 publications

Ensemble learning‐based channel estimation and hybrid precoding for millimeter‐wave massive multiple input multiple output system

Ensemble learning‐based channel estimation and hybrid precoding for millimeter‐wave massive multiple input multiple output system

A comprehensive study on channel estimation techniques using machine learning for massive MIMO systems

RETRACTED: Channel Estimation of MIMO-OFDM through Wavelet Transform to Improve the Spectral Efficiency and Data Transmission Rate

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