Machine-Learning and 3D Point-Cloud Based Signal Power Path Loss Model for the Deployment of Wireless Communication Systems

<abstract><p>Data-driven and feedback cycle-based approaches are necessary to optimize the performance of modern complex wireless communication systems. Machine learning technologies can provide solutions for these requirements. This study shows a comprehensive framework of optimizing wireless communication systems and proposes two optimal decision schemes that have not been well-investigated in existing research. The first one is supervised learning modeling and optimal decision making by optimization, and the second is a simple and implementable reinforcement learning algorithm. The proposed schemes were verified through real-world experiments and computer simulations, which revealed the necessity and validity of this research.</p></abstract>

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Closed-form formulas for the outage probability of wireless communication systems with a minimum signal power constraint

IEEE Transactions on Vehicular Technology ◽

10.1109/tvt.2002.802971 ◽

2002 ◽

Vol 51 (6) ◽

pp. 1689-1698 ◽

Cited By ~ 23

Author(s):

Hong-Chuan Yang ◽

M. Alouini

Keyword(s):

Wireless Communication ◽

Outage Probability ◽

Closed Form ◽

Communication Systems ◽

Wireless Communication Systems ◽

Signal Power ◽

Power Constraint

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

Journal of Communications ◽

10.12720/jcm.16.6.228-235 ◽

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.

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3D Point Cloud-Based Tree Canopy Visualization for a Smart Deployment of Mobile Communication Systems

10.5772/intechopen.96179 ◽

2021 ◽

Author(s):

Yunus Egi ◽

Engin Eyceyurt

Keyword(s):

Machine Learning ◽

Mobile Communication ◽

Communication Systems ◽

Point Cloud ◽

Smart Cities ◽

Signal Strength ◽

Propagation Model ◽

3D Point Cloud ◽

Mobile Communication Systems ◽

Signal Fading

Mobile communication is one of the most important parameters of smart cities in terms of maintaining connectivity and interaction between humans and smart systems. However, In the deployment process of Mobile Communication Systems (MCS), Radio Frequency (RF) engineers use location depended empirical Signal Strength Path Loss (SSPL) models ending up with poor signal strength and slow data connection. This is due to the fact that empirical propagation models usually are restrained by the environment and do not implement state of the art technologies, including Unmanned Aerial Vehicles (UAV), Light Detection and Ranging (LiDAR), Image Processing, and Machine Learning to increase efficiency. Terrains involving buildings, hills, trees, mountains, and human-made structures are considered irregular terrains by telecommunication engineers. Irregular terrains, specifically trees, significantly affect MCS’s efficiency because of their complex pattern resulting in erroneous signal fading via multi-path reflection and absorption. Therefore, a virtual 3D environment is required to extract the required 3D terrain pattern and elevation data from the environment. Once this data is processed in the machine learning algorithm, an adaptive propagation model can be formed and can significantly improve SSPL prediction accuracy for MCS. This chapter presents 3D point cloud visualization via sensor fusion and 2D image color classification techniques, which lead to a novel propagation model for the smart deployment of MCS. The proposed system’s main contribution is to develop an intelligent environment that eliminates limitations and minimizes related signal fading prediction errors. In addition, having better connectivity and efficiency will resolve the communication problem of smart cities. The chapter also provides a case study that significantly outperforms other empirical models with an accuracy of 95.4%.

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Machine Learning in Beyond 5G/6G Networks—State-of-the-Art and Future Trends

Electronics ◽

10.3390/electronics10222786 ◽

2021 ◽

Vol 10 (22) ◽

pp. 2786

Author(s):

Vasileios P. Rekkas ◽

Sotirios Sotiroudis ◽

Panagiotis Sarigiannidis ◽

Shaohua Wan ◽

George K. Karagiannidis ◽

...

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Wireless Communications ◽

Wireless Communication ◽

Communication Systems ◽

State Of The Art ◽

Wireless Communication Systems ◽

Future Trends ◽

Network Applications ◽

Open Issues

Artificial Intelligence (AI) and especially Machine Learning (ML) can play a very important role in realizing and optimizing 6G network applications. In this paper, we present a brief summary of ML methods, as well as an up-to-date review of ML approaches in 6G wireless communication systems. These methods include supervised, unsupervised and reinforcement techniques. Additionally, we discuss open issues in the field of ML for 6G networks and wireless communications in general, as well as some potential future trends to motivate further research into this area.

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Review of Recent Phased Arrays for Millimeter-Wave Wireless Communication

Sensors ◽

10.3390/s18103194 ◽

2018 ◽

Vol 18 (10) ◽

pp. 3194 ◽

Cited By ~ 34

Author(s):

Aqeel Naqvi ◽

Sungjoon Lim

Keyword(s):

Wireless Communication ◽

Millimeter Wave ◽

Communication Systems ◽

Phased Arrays ◽

Path Loss ◽

Directional Antennas ◽

Base Stations ◽

Wireless Communication Systems ◽

Data Traffic ◽

System Architectures

Owing to the rapid growth in wireless data traffic, millimeter-wave (mm-wave) communications have shown tremendous promise and are considered an attractive technique in fifth-generation (5G) wireless communication systems. However, to design robust communication systems, it is important to understand the channel dynamics with respect to space and time at these frequencies. Millimeter-wave signals are highly susceptible to blocking, and they have communication limitations owing to their poor signal attenuation compared with microwave signals. Therefore, by employing highly directional antennas, co-channel interference to or from other systems can be alleviated using line-of-sight (LOS) propagation. Because of the ability to shape, switch, or scan the propagating beam, phased arrays play an important role in advanced wireless communication systems. Beam-switching, beam-scanning, and multibeam arrays can be realized at mm-wave frequencies using analog or digital system architectures. This review article presents state-of-the-art phased arrays for mm-wave mobile terminals (MSs) and base stations (BSs), with an emphasis on beamforming arrays. We also discuss challenges and strategies used to address unfavorable path loss and blockage issues related to mm-wave applications, which sets future directions.

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