Distinction of IKr blockers based on channel state preference using voltage clamp simulations and machine learning

2021 ◽  
Vol 111 ◽  
pp. 107043
Author(s):  
Fernando Escobar Ropero ◽  
Julio Gomis-Tena Dolz ◽  
Francisco Javier Saiz Rodríguez ◽  
Lucía Romero Pérez
Keyword(s):  
Machine Learning ◽  
Voltage Clamp ◽  
Channel State ◽  
State Preference

scholarly journals Machine Learning-Based Relay Selection for Secure Transmission in Multi-Hop DF Relay Networks

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 949 ◽  
Author(s):  
Tien-Tung Nguyen ◽  
Jong-Ho Lee ◽  
Minh-Tuan Nguyen ◽  
Yong-Hwa Kim
Keyword(s):  
Machine Learning ◽  
Relay Selection ◽  
Computation Time ◽  
Selection Method ◽  
Decode And Forward ◽  
Channel State ◽  
Best Relay Selection ◽  
Cooperative Relays ◽  
Relaying Systems ◽  
Secure Transmission

A relay selection method is proposed for physical-layer security in multi-hop decode-and-forward (DF) relaying systems. In the proposed method, cooperative relays are selected to maximize the achievable secrecy rates under DF-relaying constraints by the classification method. Artificial neural networks (ANNs), which are used for machine learning, are applied to classify the set of cooperative relays based on the channel state information of all nodes. Simulation results show that the proposed method can achieve near-optimal performance for an exhaustive search method for all combinations of relay selection, while computation time are reduced significantly. Furthermore, the proposed method outperforms the best relay selection method, in which the best relay in terms of secrecy performance is selected among active ones.

scholarly journals Acquisition of channel state information for mmWave massive MIMO: traditional and machine learning-based approaches

2021 ◽  
Vol 64 (8) ◽  
Author(s):  
Chenhao Qi ◽  
Peihao Dong ◽  
Wenyan Ma ◽  
Hua Zhang ◽  
Zaichen Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Channel State Information ◽  
Massive Mimo ◽  
Channel State ◽  
State Information

BReML: A Breathing Rate Estimator Using Wi-Fi Channel State Information and Machine Learning

2021 ◽  
Author(s):  
Albany Armenta-Garcia ◽  
Felix F. Gonzalez-Navarro ◽  
Jesus Caro-Gutierrez ◽  
Brenda L. Flores-Rios ◽  
Jorge E. Ibarra-Esquer
Keyword(s):  
Machine Learning ◽  
Channel State Information ◽  
Breathing Rate ◽  
Channel State ◽  
State Information

scholarly journals Improving CSI Prediction Accuracy with Deep Echo State Networks in 5G Networks

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6475
Author(s):  
Tommaso Pecorella ◽  
Romano Fantacci ◽  
Benedetta Picano
Keyword(s):  
Machine Learning ◽  
Prediction Accuracy ◽  
Time Series Prediction ◽  
Machine Learning Techniques ◽  
Percentage Error ◽  
Channel State ◽  
Absolute Deviation ◽  
Combined Application ◽  
Echo State Networks ◽  
Learning Techniques

The forthcoming fifth-generation networks require improvements in cognitive radio intelligence, going towards more smart and aware radio systems. In the emerging radio intelligence approach, the empowerment of cognitive capabilities is performed through the adoption of machine learning techniques. This paper investigates the combined application of the convolutional and recurrent neural networks for the channel state information forecasting, providing a multivariate scalar time series prediction by taking into account the multiple factors dependence of the channel state conditions. Finally, the system performance has been analyzed in terms of prediction accuracy expressed as absolute deviation error and mean percentage error, in comparison with an alternative machine learning method recently proposed in the literature with the aim at solving the same prediction problem.

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