Massive MIMO Channel Estimation Using FastICA Weighted Function for VLC in 5G Networks

Sindhuja, Rajesh; Shankar, Arathi R

doi:10.1007/s40031-022-00850-3

Cited by 2 publications

(3 citation statements)

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“…A change in the identification of blind phase with a feedback loop has a temporal complexity that is just 1/3 that of a commercial laser with a linewidth of 200 kHz. [25] proposed a method to estimate the blind channel approach using fast independent component analysis based on the weight function (FICA-WF) for blind interference cancellation. The existing models such as parallel factor analysis, joint parallel factor analysis, STBC-m-MIMO-OFDM, and MMSE-CMA-DFCE obtained SNR ranging from 10 to 20 dB, whereas the proposed model obtained better SNR of 9.02 dB for the FastICA-WF.…”

Section: Literature Reviewmentioning

confidence: 99%

“…It is characterized by the random assignment of the weights of the input layer as well as the threshold values in the hidden layer and, hence, the training problem is translated into finding the minimum norm least-squares solution of a linear system. The basic ELM may be written as follows (25).…”

Section: Channel Estimation Using Elmmentioning

confidence: 99%

“…𝑓(𝑥) = ∑ 𝛽 𝑖 𝑔 𝑖 (𝑥) = 𝑔(𝑥)𝛽 = 𝑇 𝐿 𝑖=1(25) Where the weight of the resultant matrix is denoted by  , T represents the target output matrix, activation function. Figure4shows the network structure of ELM.…”

mentioning

confidence: 99%

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Highly Accurate Technique for CO-OFDM Channel Estimation Technique Using Extreme Learning Machine (ELM)

Joseph

Puttamadappa

2023

Wseas Transactions on Electronics

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In wireless systems, channel estimation is considered a problematic technology, due to the fact of the difference in time between wireless channels and the noise effect. Orthogonal frequency-division multiplexing (OFDM) is a promising candidate for future optical communications and has received wide concern. The article proposed a Coherent Optical (CO) orthogonal frequency division multiplexing (OFDM) scheme, which gives a scalable and flexible solution for increasing the transmission rate, being extremely robust to chromatic dispersion as well as polarization mode dispersion. Nevertheless, both coherent detection and OFDM are prone to phase noise due to the phase mismatch between the laser oscillators at the transmitter and receiver sides and the relatively long OFDM symbol duration compared to that of single carrier communications. An Extreme Learning Machine (ELM) with Pilot Assisted Equalization (PEM) is proposed for compensation of impairments caused by fibre nonlinearity in coherent optical communication systems. Channel estimation using ELM and the value of distortion is sent to the OSTBC receiving end based on the distortion information the data is decoded and pilot data is removed. FFT is applied to the data and QPSK demodulation is done in the data to get its original form. In addition, the article utilized a free-space optical communication system of multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) with a modified receiver structure. Simulation reveals that the proposed model exhibits significant BER (0.0112) performance and provides better spectral efficiency as compared with conventional systems and less computational complexity. This suggested that the proposed method shows better performance by using the CO-OFDM-FSO-MIMO-ELM-based channel estimation technique for high-speed data communication networks in real-time scenarios respectively.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Channel Estimation Using Elmmentioning

confidence: 99%