scholarly journals A Big Data Enabled Channel Model for 5G Wireless Communication Systems

2020 ◽  
Vol 6 (2) ◽  
pp. 211-222 ◽  
Author(s):  
Jie Huang ◽  
Cheng-Xiang Wang ◽  
Lu Bai ◽  
Jian Sun ◽  
Yang Yang ◽  
...  
Keyword(s):  
Big Data ◽  
Wireless Communication ◽  
Communication Systems ◽  
Channel Model ◽  
Wireless Communication Systems

scholarly journals Three-Dimensional MIMO Channel Model with High-Mobility Wireless Communication Systems Using Leaky Coaxial Cable in Rectangular Tunnel

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Kai Zhang ◽  
Fangqi Zhang ◽  
Guoxin Zheng ◽  
Lei Cang
Keyword(s):  
Wireless Communication ◽  
Communication Systems ◽  
Channel Model ◽  
Mimo System ◽  
Three Dimensional ◽  
High Mobility ◽  
Coaxial Cable ◽  
Mimo Channel ◽  
Wireless Communication Systems ◽  
Rectangular Tunnel

With the rapid development of high-mobility wireless communication systems, e.g., high-speed train (HST) and metro wireless communication systems, more and more attention has been paid to the wireless communication technology in tunnel-like scenarios. In this paper, we propose a three-dimensional (3D) nonstationary multiple-input multiple-output (MIMO) channel model with high-mobility wireless communication systems using leaky coaxial cable (LCX) inside a rectangular tunnel over the 1.8 GHz band. Taking into account single-bounce scattering under line-of-sight (LoS) and non-line-of-sight (NLoS) propagations condition, the analytical expressions of the channel impulse response (CIR) and temporal correlation function (T-CF) are derived. In the proposed channel model, it is assumed that a large number of scatterers are randomly distributed on the sidewall of the tunnel and the roof of the tunnel. We analyze the impact of various model parameters, including LCX spacing, time separation, movement velocity of Rx, and K-factor, on the T-CF of the MIMO channel model. For HST, the results of some further studies on the maximum speed of 360 km/h are given. By comparing the T-CF between the dipole MIMO system and the LCX-MIMO system, we can see that the performance of the LCX-MIMO system is better than that of the dipole MIMO system.

scholarly journals A Non-Stationary IMT-Advanced MIMO Channel Model for High-Mobility Wireless Communication Systems

2017 ◽  
Vol 16 (4) ◽  
pp. 2057-2068 ◽  
Author(s):  
Ammar Ghazal ◽  
Yi Yuan ◽  
Cheng-Xiang Wang ◽  
Yan Zhang ◽  
Qi Yao ◽  
...  
Keyword(s):  
Wireless Communication ◽  
Communication Systems ◽  
Channel Model ◽  
High Mobility ◽  
Mimo Channel ◽  
Wireless Communication Systems

Big Spectrum Data and Deep Learning Techniques for Cognitive Wireless Networks

2018 ◽  
pp. 33-60
Author(s):  
Punam Dutta Choudhury ◽  
Ankumoni Bora ◽  
Kandarpa Kumar Sarma
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Wireless Communication ◽  
Spectrum Sensing ◽  
Communication Systems ◽  
Common Factor ◽  
Wireless Communication Systems ◽  
Data Generation ◽  
Daily Lives ◽  
Communication Frameworks

The present world is data driven. From social sciences to frontiers of research in science and engineering, one common factor is the continuous data generation. It has started to affect our daily lives. Big data concepts are found to have significant impact in modern wireless communication systems. The analytical tools of big data have been identified as full scale autonomous mode of operation which necessitates a strong role to be played by learning based systems. The chapter has focused on the synergy of big data and deep learning for generating better efficiency in evolving communication frameworks. The chapter has also included discussion on machine learning and cognitive technologies w.r.t. big data and mobile communication. Cyber Physical Systems being indispensable elements of M2M communication, Wireless Sensor Networks and its role in CPS, cognitive radio networking and spectrum sensing have also been discussed. It is expected that spectrum sensing, big data and deep learning will play vital roles in enhancing the capabilities of wireless communication systems.

Big Spectrum Data and Deep Learning Techniques for Cognitive Wireless Networks

2020 ◽  
pp. 994-1015
Author(s):  
Punam Dutta Choudhury ◽  
Ankumoni Bora ◽  
Kandarpa Kumar Sarma
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Wireless Communication ◽  
Spectrum Sensing ◽  
Communication Systems ◽  
Common Factor ◽  
Wireless Communication Systems ◽  
Data Generation ◽  
Daily Lives ◽  
Communication Frameworks

The present world is data driven. From social sciences to frontiers of research in science and engineering, one common factor is the continuous data generation. It has started to affect our daily lives. Big data concepts are found to have significant impact in modern wireless communication systems. The analytical tools of big data have been identified as full scale autonomous mode of operation which necessitates a strong role to be played by learning based systems. The chapter has focused on the synergy of big data and deep learning for generating better efficiency in evolving communication frameworks. The chapter has also included discussion on machine learning and cognitive technologies w.r.t. big data and mobile communication. Cyber Physical Systems being indispensable elements of M2M communication, Wireless Sensor Networks and its role in CPS, cognitive radio networking and spectrum sensing have also been discussed. It is expected that spectrum sensing, big data and deep learning will play vital roles in enhancing the capabilities of wireless communication systems.

scholarly journals Measurement of Path Loss Characterization and Prediction Modeling for Swarm UAVs Air-to-Air Wireless Communication Systems

2021 ◽  
pp. 228-235
Author(s):  
Sarun Duangsuwan ◽  
Keyword(s):  
Wireless Communication ◽  
Prediction Model ◽  
Communication Systems ◽  
Channel Model ◽  
Path Loss ◽  
Channel Modeling ◽  
Absolute Error ◽  
Wireless Communication Systems ◽  
Prediction Errors ◽  
Artificial Neural Network Ann

A challenge swarm unmanned aerial vehicles (swarm UAVs)-based wireless communication systems have been focused on channel modeling in various environments. In this paper, we present the characterized path loss air-to-air (A2A) channel modeling-based measurement and prediction model. The channel model was considered using A2A Two-Ray (A2AT-R) extended path loss modeling. The prediction model was considered using an artificial neural network (ANN) algorithm to train the measured dataset. To evaluate the measurement result, path loss models between the A2AT-R model and the prediction model are shown. We show that the prediction model using ANN is optimal to train the measured data for the A2A channel model. To discuss the result, the parametric prediction errors such as mean absolute error (MAE), root mean square error (RMSE), and R-square (R2), are performed.

scholarly journals Channel Estimation for Large Intelligent Surface-Based Transceiver Using a Parametric Channel Model

2021 ◽  
Author(s):  
mojtaba ghermezcheshmeh ◽  
Vahid Jamali ◽  
Haris Gacanin, ◽  
Nikola zlatanov
Keyword(s):  
Wireless Communication ◽  
Channel Estimation ◽  
Communication Systems ◽  
Channel Model ◽  
Estimation Error ◽  
Computational Cost ◽  
Channel Estimation Error ◽  
Wireless Communication Systems ◽  
Physical Channel ◽  
Estimation Scheme

<div>Large intelligent surface-based transceivers (LISBTs), in which a spatially continuous surface is being used for signal transmission and reception, have emerged as a promising solution for improving the coverage and data rate of wireless communication systems. To realize these objectives, the acquisition of accurate channel state information (CSI) in LISBT-assisted wireless communication systems is crucial. In this paper, we propose a channel estimation scheme based on a parametric physical channel model for line-of-sight dominated communication in millimeter and terahertz wave bands. The proposed estimation scheme requires only five pilot signals to perfectly estimate the channel parameters assuming there is no noise at the receiver. In the presence of noise, we propose an iterative estimation algorithm that decreases the channel estimation error due to noise. The training overhead and computational cost of the proposed scheme do not scale with the number of antennas. The simulation results demonstrate that the proposed estimation scheme significantly outperforms other benchmark schemes.</div>

Map-Based Channel Model for Evaluation of 5G Wireless Communication Systems

2017 ◽  
Vol 65 (12) ◽  
pp. 6491-6504 ◽  
Author(s):  
Pekka Kyosti ◽  
Janne Lehtomaki ◽  
Jonas Medbo ◽  
Matti Latva-aho
Keyword(s):  
Wireless Communication ◽  
Communication Systems ◽  
Channel Model ◽  
Wireless Communication Systems
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