ChannelGAN: Deep Learning based Channel Modeling and Generating

Predictive Wireless Channel Modeling of MmWave Bands Using Machine Learning

Electronics ◽

10.3390/electronics10243114 ◽

2021 ◽

Vol 10 (24) ◽

pp. 3114

Author(s):

Abdallah Mobark Aldosary ◽

Saud Alhajaj Aldossari ◽

Kwang-Cheng Chen ◽

Ehab Mahmoud Mohamed ◽

Ahmed Al-Saman

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Wireless Communication ◽

Random Forests ◽

Communication Systems ◽

Channel Modeling ◽

Base Station ◽

Wireless Channel ◽

Base Stations ◽

Data Driven

The exploitation of higher millimeter wave (MmWave) is promising for wireless communication systems. The goals of machine learning (ML) and its subcategories of deep learning beyond 5G (B5G) is to learn from the data and make a prediction or a decision other than relying on the classical procedures to enhance the wireless design. The new wireless generation should be proactive and predictive to avoid the previous drawbacks in the existing wireless generations to meet the 5G target services pillars. One of the aspects of Ultra-Reliable Low Latency Communications (URLLC) is moving the data processing tasks to the cellular base stations. With the rapid usage of wireless communications devices, base stations are required to execute and make decisions to ensure communication reliability. In this paper, an efficient new methodology using ML is applied to assist base stations in predicting the frequency bands and the path loss based on a data-driven approach. The ML algorithms that are used and compared are Multilelayers Perceptrons (MLP) as a neural networks branch and Random Forests. Systems that consume different bands such as base stations in telecommunications with uplink and downlink transmissions and other internet of things (IoT) devices need an urgent response between devices to alter bands to maintain the requirements of the new radios (NR). Thus, ML techniques are needed to learn and assist a base station to fluctuate between different bands based on a data-driven system. Then, to testify the proposed idea, we compare the analysis with other deep learning methods. Furthermore, to validate the proposed models, we applied these techniques to different case studies to ensure the success of the proposed works. To enhance the accuracy of supervised data learning, we modified the random forests by combining an unsupervised algorithm to the learning process. Eventually, the superiority of ML towards wireless communication demonstrated great accuracy at 90.24%.

Download Full-text

Artificial Intelligence in Optical Communications: From Machine Learning to Deep Learning

Frontiers in Communications and Networks ◽

10.3389/frcmn.2021.656786 ◽

2021 ◽

Vol 2 ◽

Author(s):

Danshi Wang ◽

Min Zhang

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Machine Learning ◽

Deep Learning ◽

Optical Communication ◽

Optical Communications ◽

Channel Modeling ◽

Modeling Method ◽

Learning Performance ◽

Sequential Data

Techniques from artificial intelligence have been widely applied in optical communication and networks, evolving from early machine learning (ML) to the recent deep learning (DL). This paper focuses on state-of-the-art DL algorithms and aims to highlight the contributions of DL to optical communications. Considering the characteristics of different DL algorithms and data types, we review multiple DL-enabled solutions to optical communication. First, a convolutional neural network (CNN) is used for image recognition and a recurrent neural network (RNN) is applied for sequential data analysis. A variety of functions can be achieved by the corresponding DL algorithms through processing the different image data and sequential data collected from optical communication. A data-driven channel modeling method is also proposed to replace the conventional block-based modeling method and improve the end-to-end learning performance. Additionally, a generative adversarial network (GAN) is introduced for data augmentation to expand the training dataset from rare experimental data. Finally, deep reinforcement learning (DRL) is applied to perform self-configuration and adaptive allocation for optical networks.

Download Full-text

Data-driven Optical Fiber Channel Modeling: A Deep Learning Approach

Journal of Lightwave Technology ◽

10.1109/jlt.2020.2993271 ◽

2020 ◽

Vol 38 (17) ◽

pp. 4730-4743 ◽

Cited By ~ 2

Author(s):

Danshi Wang ◽

Yuchen Song ◽

Jin Li ◽

Jun Qin ◽

Tao Yang ◽

...

Keyword(s):

Deep Learning ◽

Optical Fiber ◽

Channel Modeling ◽

Data Driven ◽

Learning Approach ◽

Fiber Channel

Download Full-text

Deep Learning

10.1017/cbo9780511780295 ◽

2009 ◽

Cited By ~ 94

Author(s):

Stellan Ohlsson

Keyword(s):

Deep Learning

Download Full-text

Intelligence artificielle : le futur de l’Orthodontie ?

Revue d Orthopédie Dento-Faciale ◽

10.1051/odf/2019026 ◽

2019 ◽

Vol 53 (3) ◽

pp. 281-294

Author(s):

Jean-Michel Foucart ◽

Augustin Chavanne ◽

Jérôme Bourriau

Keyword(s):

Big Data ◽

Deep Learning ◽

Intelligence Artificielle ◽

Set Up

Nombreux sont les apports envisagés de l’Intelligence Artificielle (IA) en médecine. En orthodontie, plusieurs solutions automatisées sont disponibles depuis quelques années en imagerie par rayons X (analyse céphalométrique automatisée, analyse automatisée des voies aériennes) ou depuis quelques mois (analyse automatique des modèles numériques, set-up automatisé; CS Model +, Carestream Dental™). L’objectif de cette étude, en deux parties, est d’évaluer la fiabilité de l’analyse automatisée des modèles tant au niveau de leur numérisation que de leur segmentation. La comparaison des résultats d’analyse des modèles obtenus automatiquement et par l’intermédiaire de plusieurs orthodontistes démontre la fiabilité de l’analyse automatique; l’erreur de mesure oscillant, in fine, entre 0,08 et 1,04 mm, ce qui est non significatif et comparable avec les erreurs de mesures inter-observateurs rapportées dans la littérature. Ces résultats ouvrent ainsi de nouvelles perspectives quand à l’apport de l’IA en Orthodontie qui, basée sur le deep learning et le big data, devrait permettre, à moyen terme, d’évoluer vers une orthodontie plus préventive et plus prédictive.

Download Full-text