Channel estimation in OFDM systems using neural network technology combined with a genetic algorithm

Cheng, Chien‐Hong; Huang, Yao-Hung; Chen, Hsing‐Chung

doi:10.1007/s00500-015-1749-7

Cited by 15 publications

(8 citation statements)

References 21 publications

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“…When back-propagation algorithms are used in NN training, back-propagation neural networks (BPNNs) are created [231]. The basic algorithm concept is backward propagating the network error from the output to the input layer and adjusting the weights to reduce the network error through the steepest descent approach.…”

Section: Back-propagation Neural Networkmentioning

confidence: 99%

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Artificial intelligence for channel estimation in multicarrier systems for B5G/6G communications: a survey

Boas

Silva

Figueiredo

et al. 2022

J Wireless Com Network

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Multicarrier modulation allows for deploying wideband systems resilient to multipath fading channels, impulsive noise, and intersymbol interference compared to single-carrier systems. Despite this, multicarrier signals suffer from different types of distortion, including channel noise sources and long- and short-term fading. Consequently, the receiver must estimate the channel features and compensate it for data recovery based on channel estimation techniques, such as non-blind, blind, and semi-blind approaches. These techniques are model-based and designed with accurate mathematical channel models encompassing their features. Nevertheless, complex environments challenge accurate mathematical channel estimation modeling, which might neither be accurate nor correspond to reality. This impairment decreases the system performance due to the channel estimation accuracy loss. Fortunately, (AI) algorithms can learn the relationship among different system variables using a model-driven or model-free approach. Thereby, AI algorithms are used for channel estimation by exploiting its complexity without unrealistic assumptions, following a better performance than conventional techniques under the same channel. Hence, this paper comprehensively surveys AI-based channel estimation for multicarrier systems. First, we provide essential background on conventional channel estimation techniques in the context of multicarrier systems. Second, the AI-aided channel estimation strategies are investigated using the following approaches: classical learning, neural networks, and reinforcement learning. Lastly, we discuss current challenges and point out future research directions based on recent findings.

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Section: Back-propagation Neural Networkmentioning

confidence: 99%

“…Complex-valued BPNNs have been designed based on three layers (input, hidden, and output) of NN for channel estimation purposes, as shown in Fig. 11 [231,232]. The complex signal is decomposed into real and imaginary parts to feed-forward the network.…”

Section: Back-propagation Neural Networkmentioning

confidence: 99%

Artificial intelligence for channel estimation in multicarrier systems for B5G/6G communications: a survey

Boas

Silva

Figueiredo

et al. 2022

J Wireless Com Network

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“…The use of CNNs for channel estimation was investigated in [16], [17]. In [18], the authors combined a ANN architecture with a genetic algorithm for channel estimation. In [19], the authors used a combination of Super-resolution CNN and a feed-forward de-noising CNN to achieve performances close to MMSE methods.…”

Section: A Related State Of the Artmentioning

confidence: 99%

Channel Estimation in C-V2X using Deep Learning

Sattiraju¹,

Weinand²,

Schotten³

2020

Preprint

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Channel estimation forms one of the central component in current Orthogonal Frequency Division Multiplexing (OFDM) systems that aims to eliminate the inter-symbol interference by calculating the Channel State Information (CSI) using the pilot symbols and interpolating them across the entire timefrequency grid. It is also one of the most researched field in the Physical Layer (PHY) with Least-Squares (LS) and Minimum Mean Squared Error (MMSE) being the two most used methods. In this work, we investigate the performance of deep neural network architecture based on Convolutional Neural Networks (CNNs) for channel estimation in vehicular environments used in 3GPP Rel.14 Cellular-Vehicle-to-Everything (C-V2X) technology. To this end, we compare the performance of the proposed Deep Learning (DL) architectures to the legacy LS channel estimation currently employed in C-V2X. Initial investigations prove that the proposed DL architecture outperform the legacy C-V2X channel estimation methods especially at high mobile speeds.

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“…In [33]- [36], BPNNs were expanded into space time coded MIMO-OFDM systems and orthogonal frequency division multiplexing-interleave division multiple access (OFDM-IDMA) systems for estimating the channel coefficients. Furthermore, [37] combined the BPNN with genetic algorithm to improve the estimation performance and convergence rate, which is usually used to search for reliable solutions to optimization problems.…”

Section: A Related Workmentioning

confidence: 99%

Deep Neural Networks for Channel Estimation in Underwater Acoustic OFDM Systems

et al. 2019

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Orthogonal frequency division multiplexing (OFDM) provides a promising modulation technique for underwater acoustic (UWA) communication systems. It is indispensable to obtain channel state information for channel estimation to handle the various channel distortions and interferences. However, the conventional channel estimation methods such as least square (LS), minimum mean square error (MMSE) and back propagation neural network (BPNN) cannot be directly applied to UWA-OFDM systems, since complicated multipath channels may cause a serious decline in performance estimation. To address the issue, two types of channel estimators based on deep neural networks (DNNs) are proposed with a novel training strategy in this paper. The proposed DNN models are trained with the received pilot symbols and the correct channel impulse responses in the training process, and then the estimated channel impulse responses are offered by the proposed DNN models in the working process. The experimental results demonstrate that the proposed methods outperform LS, BPNN algorithms and are comparable to the MMSE algorithm in respect to bit error rate and normalized mean square error. Meanwhile, there is no requirement of prior statistics information about channel autocorrelation matrix and noise variance for our proposals to estimate channels in UWA-OFDM systems, which is superior to the MMSE algorithm. Our proposed DNN models achieve better performance using 16QAM than 32QAM, 64QAM, furthermore, the specified DNN architectures help improve real-time performance by saving runtime and storage resources for online UWA communications. INDEX TERMS Deep neural networks, OFDM systems, channel estimation, underwater acoustic communication.

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Channel estimation in OFDM systems using neural network technology combined with a genetic algorithm

Cited by 15 publications

References 21 publications

Artificial intelligence for channel estimation in multicarrier systems for B5G/6G communications: a survey

Artificial intelligence for channel estimation in multicarrier systems for B5G/6G communications: a survey

Channel Estimation in C-V2X using Deep Learning

Deep Neural Networks for Channel Estimation in Underwater Acoustic OFDM Systems

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