An accurate neural network approach in modeling an UWB channel in an underground mine

Zaarour, Nour; Kandil, Nahi; Hakem, Nadir

doi:10.1109/aps.2013.6711463

Cited by 5 publications

(2 citation statements)

References 5 publications

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“…To avoid the vanishing gradient problem, a single layer-based MLP network is proposed in [75] to obtain the path loss of ultra wideband (UWB) channels with the frequency band from 0.875 MHz to 10 GHz. Instead of using a generalized MLP network, an RBF-based neural network is exploited in [76], [77] to capture the frequency-dependent path loss of the UWB channels and estimate the received power. The Radial Basis Function (RBF)-based network used in [76], [77] is a specialized case of the MLP network which contains only three layers: an input layer, a hidden layer with a nonlinear RBF activation function, and a linear output layer.…”

Section: B Ml-enabled Channel Modeling and Predictionmentioning

confidence: 99%

Artificial Intelligence Enabled Radio Propagation for Communications—Part II: Scenario Identification and Channel Modeling

Huang

et al. 2022

IEEE Trans. Antennas Propagat.

105

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This two-part paper investigates the application of artificial intelligence (AI) and in particular machine learning (ML) to the study of wireless propagation channels. In Part I, we introduced AI and ML as well as provided a comprehensive survey on ML enabled channel characterization and antennachannel optimization, and in this part (Part II) we review state-ofthe-art literature on scenario identification and channel modeling here. In particular, the key ideas of ML for scenario identification and channel modeling/prediction are presented, and the widely used ML methods for propagation scenario identification and channel modeling and prediction are analyzed and compared. Based on the state-of-art, the future challenges of AI/ML-based channel data processing techniques are given as well.

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Section: B Ml-enabled Channel Modeling and Predictionmentioning

confidence: 99%

Artificial Intelligence Enabled Radio Propagation for Communications—Part II: Scenario Identification and Channel Modeling

Huang

et al. 2022

IEEE Trans. Antennas Propagat.

105

View full text Add to dashboard Cite

show abstract

“…The other one is to predict the channel characteristics based on ML algorithms which can dig the mapping relationship between physical environment information and the channel characteristics. The function between frequency, distance, and path loss (PL) was modeled by two types of artificial neural networks (ANNs), i.e., multilayer perceptron (MLP) and radial basis function (RBF) [30][31][32][33][34]. In [35], PL was also modeled as a mapping relationship between delay and the atmosphere by MLP.…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

Predicting Wireless MmWave Massive MIMO Channel Characteristics Using Machine Learning Algorithms

Bai

Wang

Huang

et al. 2018

Wireless Communications and Mobile Computing

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This paper proposes a procedure of predicting channel characteristics based on a well-known machine learning (ML) algorithm and convolutional neural network (CNN), for three-dimensional (3D) millimetre wave (mmWave) massive multiple-input multiple-output (MIMO) indoor channels. The channel parameters, such as amplitude, delay, azimuth angle of departure (AAoD), elevation angle of departure (EAoD), azimuth angle of arrival (AAoA), and elevation angle of arrival (EAoA), are generated by a ray tracing software. After the data preprocessing, we can obtain the channel statistical characteristics (including expectations and spreads of the above-mentioned parameters) to train the CNN. The channel statistical characteristics of any subchannels in a specified indoor scenario can be predicted when the location information of the transmitter (Tx) antenna and receiver (Rx) antenna is input into the CNN trained by limited data. The predicted channel statistical characteristics can well fit the real channel statistical characteristics. The probability density functions (PDFs) of error square and root mean square errors (RMSEs) of channel statistical characteristics are also analyzed.

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Channel Scenario Extensions, Identifications, and Adaptive Modeling for 6G Wireless Communications

Zhou,

Wang,

Huang

et al. 2024

IEEE Internet Things J.

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An accurate neural network approach in modeling an UWB channel in an underground mine

Cited by 5 publications

References 5 publications

Artificial Intelligence Enabled Radio Propagation for Communications—Part II: Scenario Identification and Channel Modeling

Artificial Intelligence Enabled Radio Propagation for Communications—Part II: Scenario Identification and Channel Modeling

Predicting Wireless MmWave Massive MIMO Channel Characteristics Using Machine Learning Algorithms

Channel Scenario Extensions, Identifications, and Adaptive Modeling for 6G Wireless Communications

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