Adaptive Equalization for QAM Signals Using Gated Recycle Unit Neural Network

Shi, Hongtai; Yan, Tianfeng

doi:10.1109/ctisc52352.2021.00046

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…For a long time, NNs have been employed for channel equalization [18,[23][24][25], therefore it appears reasonable to use DNN as an end-to-end approach for optimizing channel equalization and decoding simultaneously.…”

Section: A Related Workmentioning

confidence: 99%

“…𝑏 𝑙 (𝑡 + 1) = 𝑏 𝑙 (𝑡) − 𝛼 𝑐 θ (t) √q b (t)+φ (25) If the gradients over multiple iterations are consistent, a moving average of the gradient can be used to gain momentum with parameter updates in a particular direction. If the gradients are noisy, the moving average of the gradient will be smaller, leading to smaller parameter updates.…”

Section: A Optimization Algorithmsmentioning

confidence: 99%

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Deep Learning-Based SC-FDMA Channel Equalization

Mohamed

2024

IJEETC

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It is very challenging to design an effective wireless communication system. That’s because of numerous factors affecting the performance of a typical wireless communication system, such as nonlinear channel distortions and impairments. single carrier frequency division multiple access (SC-FDMA) is a multiple access scheme that is an important part of the long-term evolution (LTE) standard for uplink transmission. An advanced mobile radio system’s multiple access schemes should indeed meet stringent requirements, such as a low bit error rate (BER). In this article, we investigate the equalization problem for nonlinear channel distortions and impairments using deep neural networks (NN). We introduce a novel combined deep neural network channel equalization and symbol detection scheme based on a deep learning (DL) recurrent feedback (RF) long short-term memory (LSTM) neural network to achieve blind equalization and decoding for SC-FDMA systems without knowing the channel state information (CSI). To train the model efficiently, the training data is gathered by simulation, with channel effects and noise treated as a complete black box. CSI and constellation demapping are learned by a deep neural network (DNN) model. Then, the frequency-domain sequences that have been corrupted are implicitly equalized to get the broadcasted signal back. Our specified SC-FDMA system, which uses a quadrature phase-shift keying (QPSK) modulation method and the suggested Deep Learning-based model channel equalizer, performs better than the existing equalizers by an average of 1 to 4 dB at moderate signal-to-noise (SNR) ratios, according to simulation data. A complexity comparison between the proposed and the conventional equalizers was conducted in terms of training time, execution time, and number of operations. On combined channel equalization and symbol detection, the suggested system delivers state-of-the-art performance.

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Section: A Related Workmentioning

confidence: 99%

Section: A Optimization Algorithmsmentioning

confidence: 99%

Deep Learning-Based SC-FDMA Channel Equalization

Mohamed

2024

IJEETC

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“…Several studies have indicated that evolutionary computing-based algorithms are capable of training neural networks for nonlinear channel equalization problems to overcome the limitations of gradient-based algorithms [32][33][34][35][36]. Shi et al [37] proposed gated recycle unit neural networks for the equalization of QAM signals. The estimation of MIMO channels using feedback neural networks is performed by using three-layer neural networks by Zhang et al [38] and has been well presented.…”

Section: Introductionmentioning

confidence: 99%

Application of Bat Algorithm and Its Modified Form Trained with ANN in Channel Equalization

Mohapatra¹,

Rout²,

Bisoy

et al. 2022

Symmetry

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The transmission of high-speed data over communication channels is the function of digital communication systems. Due to linear and nonlinear distortions, data transmitted through this process is distorted. In a communication system, the channel is the medium through which signals are transmitted. The useful signal received at the receiver becomes corrupted because it is associated with noise, ISI, CCI, etc. The equalizers function at the front end of the receiver to eliminate these factors, and they are designed to make them work efficiently with proper network topology and parameters. In the case of highly dispersive and nonlinear channels, it is well known that neural network-based equalizers are more effective than linear equalizers, which use finite impulse response filters. An alternative approach to training neural network-based equalizers is to use metaheuristic algorithms. Here, in this work, to develop the symmetry-based efficient channel equalization in wireless communication, this paper proposes a modified form of bat algorithm trained with ANN for channel equalization. It adopts a population-based and local search algorithm to exploit the advantages of bats’ echolocation. The foremost initiative is to boost the flexibility of both the variants of the proposed algorithm and the utilization of proper weight, topology, and the transfer function of ANN in channel equalization. To evaluate the equalizer’s performance, MSE and BER can be calculated by considering popular nonlinear channels and adding nonlinearities. Experimental and statistical analyses show that, in comparison with the bat as well as variants of the bat and state-of-the-art algorithms, the proposed algorithm substantially outperforms them significantly, based on MSE and BER.

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