Training Based Channel Estimation for Multitaper GFDM System

Bandari, Shravan Kumar; Vakamulla, Venkata Mani; Drosopoulos, A.

doi:10.1155/2017/4747256

Cited by 8 publications

(5 citation statements)

References 14 publications

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“…The received signal is divided by channel estimation as in Eq. ( 12) to recover the transmitted signal [25].…”

Section: Channel Estimationmentioning

confidence: 99%

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Intelligent Optimization of PC-GFDM Transceiver

2023

IJIES

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Generalized frequency division multiplexing (GFDM) presents an outstanding performance in out-of-band, latency, and peak-to-average power ratio, Thus, promoting it to be the most contender for the fifth generation and beyond. This paper evaluates the performance of the GFDM transceiver system with polar code (PC) and interleavers based on the bit error rate (BER). The proposed system is enhanced by artificial intelligence, which is the genetic algorithm (GA) used to optimize the parameters of the pulse shaping filter (G) and the artificial neural network (ANN) used in improving method-based least square (LS) channel estimation dataset. At each step of evaluating the performance computed based on BER vs. SNR. The optimized PC-GFDM enhances over standard GFDM by 0.0649 in BER at 28 SNR.

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“…The received signal is divided by channel estimation as in Eq. ( 12) to recover the transmitted signal [25].…”

Section: Channel Estimationmentioning

confidence: 99%

“…The new steepest descent direction is obtained by conjugating the new and previous search directions as Eqs. (24,25) [29].…”

Section: Annmentioning

confidence: 99%

Intelligent Optimization of PC-GFDM Transceiver

2023

IJIES

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“…Adaptive filters perform correctly in many applications, such as seismology, biomedical, radar, and communications. In general, it works according to one basic principle: compute the error between the received pilot and desired pilot, which is used to estimate the channel according to the step size value (Rasadia 2012) (Bandari 2017). The ISI is the main challenge of degradation problems of wireless communication channels.…”

Section: Introductionmentioning

confidence: 99%

GFDM Transceiver Based on Ann Channel Estimation

Ali¹,

Hreshee²

2023

Kufa J. Eng.

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Generalized frequency division multiplexing (GFDM) is one of the candidate waveforms beyond 5G for wireless communication. Channel estimation is challenging in wireless transmission because of inherent interference. It is based on a pilot signal that uses Least Square (LS) and Normalized Least Mean Square (NLMS) to perform the estimation. This paper used the Artificial Neural Network (ANN) as a channel estimation method, considered a novel estimation process for the GFDM transceiver. The channel estimation based on ANN depends on the data set generated from LS estimation, which considers the proposed method is optimized to LS based on the backpropagation neural network. The ANN algorithm considered a fitness function to estimate the channel. Levenberg-Marquardt backpropagation (LM), Bayesian Regularization backpropagation (BR), and Scaled Conjugate Gradient backpropagation (SCG) training methods training by using the Matlab toolbox used to perform the estimation. BR training method gives the best performance than LS and other training methods at 22 neurons in hidden layers, which at 20dB give BER = 0.0369 that enhanced over LS by 0.08.

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“…Thus, this motivated us to employ the subject method to GFDM system. A similar method also has been performed in a GFDM system [15]. However, the DFT-based channel estimation method is performed under 2-tap random channel in which this condition is not suitable for 5G scenarios.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of GFDM Channel Estimation Using DFT for Tactile Internet Application

Permana

Hamid

2021

Electronics

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In this work, discrete Fourier transform (DFT)-based channel estimation is proposed in generalized frequency division multiplexing (GFDM) system. In the GFDM system, the subcarriers are non-orthogonal; therefore, the pilot symbols cannot be easily observed due to the interference from data symbols and noise. The proposed method can improve the channel estimation of least square (LS) method by eliminating channel impulse response outside the number of actual impulse response. First, the received signal is demodulated using zero forcing demodulator. Then, it is divided with transmitted pilot symbols to obtain channel response. Interpolation in frequency and time domains is conducted to acquire channel response for all GFDM blocks. Finally, the channel estimation algorithm using DFT is performed. The parameters of the system are adjusted so that they are suitable for tactile internet application. The channel model used is NYUSIM, which utilizes mmWave. Three scenarios in NYUSIM such as urban microcell, urban macro cell and rural macro cell are used and power delay profiles generated from NYUSIM simulator are employed in this system. The results show that mean square error (MSE) from DFT-based channel estimation gives substantial improvement for all scenarios. In addition, symbol error rate (SER) of DFT-based channel estimation provides a slight improvement of 1.5 dB than LS channel estimation.

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Training Based Channel Estimation for Multitaper GFDM System

Cited by 8 publications

References 14 publications

Intelligent Optimization of PC-GFDM Transceiver

Intelligent Optimization of PC-GFDM Transceiver

GFDM Transceiver Based on Ann Channel Estimation

Performance Evaluation of GFDM Channel Estimation Using DFT for Tactile Internet Application

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