A review of wireless channel estimation techniques: challenges and solutions

Drakshayini, M N; Kounte, Manjunath R

doi:10.1504/ijwmc.2022.126390

Cited by 8 publications

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“…Properly reshaped versions of these variables are denoted as

\boldsymbol{xtrain} CNN, ytrainCNN,\boldsymbol{xtest} CNN,\mathrm{and}\ ytestCNN.

32 …”

Section: Basic Working Of the Cnn Classifiermentioning

confidence: 99%

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Estimation of Rayleigh flat channel coefficients using deep learning

NanjundaShetty

Kounte

2022

Trans Emerging Tel Tech

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Channel estimation is a significant prerequisite in wireless communication, especially where the multipath radio propagation incurs significant fading in a noisy environment. In such a scenario, Rayleigh fading model is traditionally adopted to represent the communication channel. In this article, a new method of getting the Rayleigh flat channel coefficients using deep learning is presented.Here, we presume that the channel state and the corresponding channel coefficients remain constant in a given communication context which depends on the locations of transmitter/receiver, time of the day and the communication environment. When the context changes, the channel state also changes and the corresponding coefficients switch to the respective matching values. Thus the channel coefficients can have several possible realizations or classes. In our scheme, the deep learning network, after training, acts as a classifier to detect the class or the context of the channel state and based on that determines the corresponding channel coefficients. In our proposed method, the percentage reduction in the percentage error is about 26% of that of its nearest competitor when the channel SNR is 10 dB.

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“…Properly reshaped versions of these variables are denoted as

\boldsymbol{xtrain} CNN, ytrainCNN,\boldsymbol{xtest} CNN,\mathrm{and}\ ytestCNN.

32 …”

Section: Basic Working Of the Cnn Classifiermentioning

confidence: 99%

“…For the correct training, xtrain, ytrain, xtest, and ytest are to be reshaped (change the array format). Properly reshaped versions of these variables are denoted as xtrainCNN, ytrainCNN, xtestCNN, and ytestCNN 32.…”

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confidence: 99%

Estimation of Rayleigh flat channel coefficients using deep learning

NanjundaShetty

Kounte

2022

Trans Emerging Tel Tech

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Design of Deep Learning-Based Intelligent Blind Direct Sequence Code Division Multiple (DS-CDMA) Access Receiver

Drakshayini,

Kounte

2024

Lecture Notes in Networks and Systems

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An efficient deep neural network channel state estimator for OFDM wireless systems

Hassan,

Mohamed,

Shaaban

et al. 2023

Wireless Netw

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Channel state estimation (CSE) is essential for orthogonal frequency division multiplexing (OFDM) wireless systems to deal with multipath channel fading. To attain a high data rate with the use of OFDM technology, an efficient CSE and accurate signal detection are required. The use of machine learning (ML) to improve channel estimates has attracted a lot of attention lately. This is because ML techniques are more adaptable than traditional model-based estimation techniques. The present study proposes a receiver for low-spectrum usage in OFDM wireless systems on Rayleigh fading channels using deep learning (DL) long short-term memory (LSTM). Before online deployment and data retrieval, the proposed DL LSTM estimator gathers channel state information from transmit/receive pairs using offline training. Based on the simulation results of a comparative study, the proposed estimator outperforms conventional channel estimation approaches like minimum mean square error and least squares in noisy and interfering wireless channels. Furthermore, the proposed estimator outperforms the DL bidirectional LSTM (BiLSTM)-based CSE model. In particular, the proposed CSE performs better than other examined estimators with a reduced number of pilots, no cycle prefixes, and no prior knowledge of channel statistics. Because the proposed estimator relies on a DL neural network approach, it holds promise for OFDM wireless communication systems.

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A review of wireless channel estimation techniques: challenges and solutions

Cited by 8 publications

References 0 publications

Estimation of Rayleigh flat channel coefficients using deep learning

Estimation of Rayleigh flat channel coefficients using deep learning

Design of Deep Learning-Based Intelligent Blind Direct Sequence Code Division Multiple (DS-CDMA) Access Receiver

An efficient deep neural network channel state estimator for OFDM wireless systems

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