Deep Learning for Channel Estimation and Signal Detection in OFDM-Based Communication Systems

Wong, Kah Jing; Juwono, Filbert H.; Reine, Regina

doi:10.26418/elkha.v14i1.53962

Cited by 4 publications

(5 citation statements)

References 24 publications

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“…The proposed blind channel estimation (CE) method provides a 9.77% increase in the spectral efficiency, compared to the second best method, superimposed trainingbased CE, at 20 dB signal-to-noise ratio (SNR) and 160 km/h relative speed, for 64-Quadrature Amplitude Modulation (QAM) Direct Current-Biased Optical Orthogonal Frequency Division Multiplexing (DCO-OFDM). [27] examine the deep neural network (DNN) layers created from longshort-term memory (LSTM) for detecting the signals by learning the received signal as well as channel information. We investigate the performance of the system under various conditions.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Here, for the ELM the secret layer has been shown, i th resultant hidden node is 𝑥 𝑖 , the parameters of the i th has hidden node is 𝑤 1 and 𝑏 1 . The fundamental ELM can be written in equation (27) as ELM training approach creates a model for single hidden layer sigmoid neural networks 𝑌 as a particular case is defined in equation (28).…”

Section:    mentioning

confidence: 99%

“…𝑠̂𝑘 = {𝑠̂𝑘[1], ⋯ , 𝑠̂𝑘[𝑁 𝑠 ]} 𝑡 denotes the detected data symbols at the output layer of the ELM network. It is worth noting that 𝑤 𝑖 and 𝑏 𝑖 in(27) are fixed after the ELM training and reused for further processing.…”

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:    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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“…The interference received by the 5G AP due to transmission from the FSS earth station is calculated ITU-R.P 452-17 propagation model on prediction procedure for the evaluation of interference between stations on the surface of the Earth at frequencies above about 0.1 GHz [12] 𝐼 = 𝑃 𝑇,𝐸𝑆 + 𝐺 𝑇,𝐸𝑆 + 𝐺 𝑅,5𝐺 − 𝐿 𝑏𝑓𝑠𝑔 (7) where GR,5G is the gain of 5G AP, and Lbfsg is path loss due to free-space propagation and attenuation due to atmospheric gases (dB) [13]. The dRSS of the 5G AP is similar to (6), namely:…”

Section: B Calculations For Interference Received By 5g Ap From Earth...mentioning

confidence: 99%

Interference Analysis Between 5G System and Fixed Satellite Service in the 28 GHz Band

Ghifari,

Sari,

Mardian W

et al. 2023

ELKHA

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One of the most favorable frequency bands for 5G technology is the 27.5 - 28.5 GHz band which has been used by Fixed Satellite System (FSS) service in the uplink direction. This potentially causes interference between the two systems. This study aims to analyze the interference that occurs between satellite earth stations and 5G access points (AP), and between 5G AP and satellite sky stations. The analysis is carried out based on simulations using the Spectrum Engineering Advanced Monte Carlo Analysis Tool (SEAMCAT) software, with two scenarios. The first scenario is to analyze interference between the 5G AP and the FSS sky station. With the C/I interference criterion of 40.2 dB, the simulation results show that the 5G AP will not interfere with the FSS sky station. The second scenario is the interference simulation between FSS earth station and 5G AP. The simulation is carried out by varying the distance between the earth station and the 5G AP, the height of the earth station, and the height of the 5G AP. Based on the simulations, it is shown that the FSS earth station can interfere with 5G AP with a probability of up to 60%, so it is necessary to adjust the distance between systems and the height of the antenna to minimize the interference. The shortest distance needed to minimize interference is 36 km with an earth station height of 5 meters.

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“…Their study leverages deep neural network (DNN) layers, incorporating long-short-term memory (LSTM) units, to discern signals by acquiring knowledge from both the received signal and channel information. The outcomes of their simulations indicate that the system attains a signal bit error rate that is on par with, and in some cases surpasses, that achieved by conventional techniques [22].…”

mentioning

confidence: 99%

Conjugate Gradient Least Squares Algorithm for OFDM Nonlinear Equalization in Intelligent Future IoT Transportation Systems

Attar,

Aljaidi,

Alrosan

et al. 2023

Preprint

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Intelligent transportation systems (ITS) have recently evolved rapidly, which requires development of highly trustworthy and effective communication technologies for uses, including vehicle-to-vehicle (V2V) and vehicle-to-everything (V2X) communication; where Orthogonal Frequency Division Multiplexing (OFDM) is regarded as a strong candidate and highly popular option technique among these methods. However, the movement of vehicles introduces Doppler frequencies which produce inter-carrier interference (ICI), that is frequently occurs in V2X channels. This interference has the potential to compromise the integrity of subcarrier orthogonality within OFDM, leading to lower-quality communication and an increased likelihood of data transmission errors. When employing channels with doubly dispersive fading, OFDM necessitates the usage of a complex equalization based on the minimum mean-square error (MMSE) equalizer, which requires channel matrix inversion. Several low-complexity equalizers for OFDM have been developed and are based on band factorization, time domain LSQR (Least-Square QR) iterative computing, and banded minimum mean squared error (BMMSE). This paper proposes Conjugate Gradient Least Squares (CGLS), which is a novel iterative computation algorithm integrated with nonlinear equalizers. The suggested nonlinear equalization technique determines the trade-off between computations and performance. According to simulation data, the suggested nonlinear equalizer performs better than the current BMMSE and linear CGLS algorithms across doubly dispersive fading channels.

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Deep Learning for Channel Estimation and Signal Detection in OFDM-Based Communication Systems

Cited by 4 publications

References 24 publications

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

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

Interference Analysis Between 5G System and Fixed Satellite Service in the 28 GHz Band

Conjugate Gradient Least Squares Algorithm for OFDM Nonlinear Equalization in Intelligent Future IoT Transportation Systems

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