Performance comparison of blind and non-blind channel equalizers using artificial neural networks

Ranhotra, Sarvraj Singh; Kumar, Atul; Magarini, Maurizio; Mishra, Amit

doi:10.1109/icufn.2017.7993785

Cited by 13 publications

(8 citation statements)

References 20 publications

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“…In our first set of experiments, we compared our model to the baseline algorithms at various noise levels, using the following non-minimum phase channels taken from [11], [24], [26], We generated 2000 QPSK random symbols as the training set. Then we applied convolution with the channel impulse response, and added white Gaussian noise at a signal to noise ratio (SNR) in the range 0dB -10dB.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…In our first set of experiments, we compared our model to the baseline algorithms at various noise levels, using the following non-minimum phase channels taken from [11], [24], [26], present SER results for h 1 , h 2 and h 3 , respectively. As can be seen, the new VAEBCE significantly outperforms the baseline blind equalizers, and is quite close to the performance of the non-blind adaptive MMSE.…”

Section: Simulation Resultsmentioning

confidence: 99%

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Blind Channel Equalization Using Variational Autoencoders

Caciularu

Burshtein

2018

2018 IEEE International Conference on Communications Workshops (ICC Workshops)

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A new maximum likelihood estimation approach for blind channel equalization, using variational autoencoders (VAEs), is introduced. Significant and consistent improvements in the error rate of the reconstructed symbols, compared to constant modulus equalizers, are demonstrated. In fact, for the channels that were examined, the performance of the new VAE blind channel equalizer was close to the performance of a nonblind adaptive linear minimum mean square error equalizer. The new equalization method enables a significantly lower latency channel acquisition compared to the constant modulus algorithm (CMA). The VAE uses a convolutional neural network with two layers and a very small number of free parameters. Although the computational complexity of the new equalizer is higher compared to CMA, it is still reasonable, and the number of free parameters to estimate is small.

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Section: Simulation Resultsmentioning

confidence: 99%

“…In our first set of experiments, we compared our model to the baseline algorithms at various noise levels, using the following non-minimum phase channels taken from [11], [24], [26], present SER results for h 1 , h 2 and h 3 , respectively. As can be seen, the new VAEBCE significantly outperforms the baseline blind equalizers, and is quite close to the performance of the non-blind adaptive MMSE.…”

Section: Simulation Resultsmentioning

confidence: 99%

Blind Channel Equalization Using Variational Autoencoders

Caciularu

Burshtein

2018

2018 IEEE International Conference on Communications Workshops (ICC Workshops)

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“…In the last few years, FLANN is very famous [52]. It is a single-layer NN that can form complex decision boundaries.…”

Section: Related Workmentioning

confidence: 99%

Performance Evaluation of Machine Learning-Based Channel Equalization Techniques: New Trends and Challenges

et al. 2022

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Wireless communication systems have evolved and offered more smart and advanced systems like ad hoc and sensor-based infrastructure fewer networks. These networks are evaluated with two fundamental parameters including data rate and spectral efficiency. To achieve a high data rate and robust wireless communication, the most significant task is channel equalization at the receiver side. The transmitted data symbols when passing through the wireless channel suffer from various types of impairments, such as fading, Doppler shifts, and Intersymbol Interference (ISI), and degraded the overall network performance. To mitigate channel-related impairments, many channel equalization algorithms have been proposed for communication systems. The channel equalization problem can also be solved as a classification problem by using Machine Learning (ML) methods. In this paper, channel equalization is performed by using ML techniques in terms of Bit Error Rate (BER) analysis and comparison. Radial Basis Functions (RBFs), Multilayer Perceptron (MLP), Support Vector Machines (SVM), Functional Link Artificial Neural Network (FLANN), Long-Short Term Memory (LSTM), and Polynomial-based Neural Networks (NNs) are adopted for channel equalization.

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“…The use of ANN in developing the algorithm has the benefit of accuracy. Also, ANN is equipped with the uniqueness of concurrent processing, can learn and recall data relationships, and mapping of non-linear instances [34,35]. It is also used in several mathematical calculations [36].…”

Section: Introductionmentioning

confidence: 99%

Developing an Artificial Neural Network Algorithm for Generalized Singular Value Decomposition-based Linear Discriminant Analysis

Paredes¹,

Sison²,

Medina³

2018

International Journal on Advanced Science, Engineering and Information Technology

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Artificial Neural Networks (ANN) form a dynamic architecture for machine learning and have attained significant capabilities in various fields. It is a combination of interrelated calculation elements and derives outputs for new inputs after being trained. This study introduced a new mechanism utilizing ANN which was trained using Bayesian Regularization Back Propagation (BRBP) to improve the computational cost problem of the existing algorithm of the Generalized Singular Value Decomposition-based Linear Discriminant Analysis (LDA/GSVD). The proposed approach can minimize the number of iterations and mathematical processes of the existing LDA/GSVD algorithm which suffers time complexity. Through simulation using BLE RSSI Dataset from UCI which has 105 classes and 13 dimensions with 1420 instances, it was found out that ANN improved the computational cost during the classification of the data up to 57.14% while maintaining its accuracy. This new technique is recommended when classifying big data, and for pattern analysis as well.

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Performance comparison of blind and non-blind channel equalizers using artificial neural networks

Cited by 13 publications

References 20 publications

Blind Channel Equalization Using Variational Autoencoders

Blind Channel Equalization Using Variational Autoencoders

Performance Evaluation of Machine Learning-Based Channel Equalization Techniques: New Trends and Challenges

Developing an Artificial Neural Network Algorithm for Generalized Singular Value Decomposition-based Linear Discriminant Analysis

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