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Artificial Neural Network based Path Loss Prediction for Wireless Communication Network
2020
IEEE Access
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a theoretical basis for wireless network optimization and communication system design. ...
Meanwhile, with the rapid development of big data, cloud computing and artificial intelligence, intelligent communication is the mainstream trend of 5G and future wireless communication systems. ...
doi:10.1109/access.2020.3035209
fatcat:e4xzmanapbf4lfrzbhiuu5foam
Application of Computational Intelligence Algorithms in Radio Propagation: A Systematic Review and Metadata Analysis
2021
Mobile Information Systems
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The importance of wireless path loss prediction and interference minimization studies in various environments cannot be over-emphasized. ...
In particular, we cover artificial neural networks (ANNs), fuzzy inference systems (FISs), swarm intelligence (SI), and other computational techniques. ...
[47] developed a path loss prediction model for railway environments using an artificial neural network model based on the backpropagation network (BPN) architecture. ...
doi:10.1155/2021/6619364
fatcat:mx6o6rfsqbhpfnuqcedtpunrwm
Relay Selection for 5G New Radio Via Artificial Neural Networks
[article]
2020
arXiv
pre-print
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In this paper, we apply classification techniques using ANN with multilayer perception to predict the path loss of multiple transmitted links and base on a certain loss level, and thus execute effective ...
A alternative approach toward relay selection is to take advantage of existing operating data and apply appropriate artificial neural networks (ANN) and deep learning algorithms to alleviate severe fading ...
ACKNOWLEDGEMENT Saud Aldossari expresses a great appreciation to Prince Sattam bin Abdulaziz University for their support of providing scholarship. K.-C. ...
arXiv:2005.00741v1
fatcat:v2iwe33rjzb63op6ievaf4damu
A Generalized Regression Neural Network Approach to Wireless Signal Strength Prediction
2020
Zenodo
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This study presents a Generalized Regression Neural network GRNN based approach to wireless communication network field strength prediction. ...
-6470, Volume-4 | Issue-3 , April 2020, URL: https://www.ijtsrd.com/papers/ijtsrd30501.pdf Paper Url :https://www.ijtsrd.com/computer-science/artificial-intelligence/30501/a-generalized-regression-neural-network-approach-to-wireless-signal-strength-prediction ...
mobile communication network field strength prediction. ...
doi:10.5281/zenodo.3892767
fatcat:4lxone7fbndrbjwfyz7bqwr5la
AI-Empowered Propagation Prediction and Optimization for Reconfigurable Wireless Networks
2022
Wireless Communications and Mobile Computing
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Initially, a path loss model based on a multilayer perception neural network is established at 2.6 GHz for three base stations in an urban environment. ...
Then, this validated model is utilized to realize a coverage prediction for 20 base stations only within 1 minute. ...
(i) A path loss model based on a multilayer perception (MLP) neural network is established at 2.6 GHz for three base stations in a typical urban environment. ...
doi:10.1155/2022/9901960
fatcat:ztgwqghmurb2jksjstwabvy5vu
A Review on Delay Prediction Techniques in MANET
2014
International Journal of Computer Applications
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For wireless ad hoc networks, routing is much more complex than in traditional wireless systems, due to the lack of centralized control, infrastructure less nature and knowledge of a predetermined topology ...
This paper focusses on different methods adopted by different scientists for estimation and prediction of delay. ...
Some of the advantages of modelling using artificial neural networks are: (i) Artificial neural networks provide the potential to identify and classify network activity based on limited and incomplete ...
doi:10.5120/18978-0394
fatcat:6uljuhr2wvegnjbvr6mmbpbmee
Development of A Wi-Fi Based Indoor Location System Using Artificial Intelligence Techniques
2020
Figshare
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It has been seen that the outputs produced by the trained neural network are much more successful and reliable than the path-loss calculation. ...
Although the use of the GPS system, which requires satellite communication as an open space location solution, is very common, it cannot provide a solution for indoor. ...
Neural Network application of Matlab program has been used to create artificial neural network model. Figure 6 indicates the structure of the artificial neural network developed. ...
doi:10.6084/m9.figshare.11510433
fatcat:5i32qnebr5bxzbvoivj5qrxd5i
Comparative Analysis of Major Machine-Learning-Based Path Loss Models for Enclosed Indoor Channels
2022
Sensors
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neural networks (ANN), and artificial recurrent neural networks (RNN). ...
RNNs are mainly based on long short-term memory (LSTM). The models are compared based on measurement data to provide the best fitting machine-learning-based path loss prediction models. ...
Artificial Neural Network Model Artificial neural networks have been developed based on biological neural network functionality. ...
doi:10.3390/s22134967
pmid:35808457
pmcid:PMC9269839
fatcat:lipwa7pfqba47dwub22c74utla
Development of A Fully Data-Driven Artificial Intelligence and Deep Learning for URLLC Application in 6G Wireless Systems: A Survey
[article]
2021
arXiv
pre-print
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Artificial Intelligence provides a new technique to design wireless networks by apply learning, predicting, and make decisions to manage the stream of big data training individuals, which provides more ...
The key constraint is the amount of computing power available to spread massive data and well-designed artificial neural networks. ...
Figure 3 depicts the AI framework for wireless communication and mobility management, which is based on deep neural networks. ...
arXiv:2108.10076v1
fatcat:b753qbfwjrdujca6spguxobaxq
Predictive Wireless Channel Modeling of MmWave Bands Using Machine Learning
2021
Electronics
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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 exploitation of higher millimeter wave (MmWave) is promising for wireless communication systems. ...
The main goal of the current study is to assist the base stations to predict the channel state information such as frequency bands and the path loss using ML techniques, for instance artificial neural ...
doi:10.3390/electronics10243114
fatcat:2kzz4edeefcq5je3vijkadpzia
QoS Routing in Mobile Ad-hoc Networks using Agents
2012
International Journal of Smart Sensor and Adhoc Network.
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Fuzzy Logic (FL) were used for QoS prediction from current uncertain QoS constraints in the network for particular applications, from these uncertain constraints there is an error at the QoS prediction ...
Quality of Service (QoS) provisioning in a Mobile Ad-hoc Network (MANETs) routing for multimedia traffic is a challenging task due to dynamic topology of such networks. ...
In this, we used artificial intelligence for selection of best QoS path for multimedia communication. The fuzzy system approximates more efficiently. ...
doi:10.47893/ijssan.2012.1077
fatcat:6pbgamur2jd3phho7zm4oeumya
Predicting path loss distribution of an area from satellite images using deep learning
2020
IEEE Access
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Path loss prediction is essential for network planning in any wireless communication system. ...
In this paper, we present a fundamentally different approach for path loss distribution prediction directly from 2D satellite images based on deep convolutional neural networks. ...
Various models have been suggested to estimate path loss for wireless communication networks. ...
doi:10.1109/access.2020.2985929
fatcat:luelmfc2k5f6fh5fmnu57zesie
WIRELESS SENSOR NETWORKS CONGESTION AND ROLE OF ARTIFICIAL INTELLIGENCE
2019
INTERNATIONAL JOURNAL OF COMPUTER ENGINEERING & TECHNOLOGY
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Our paper provides opening to wireless sensor network as well as for artificial intelligence, which aims to act both proactively, in order to avoid the creation of congestion in WSNs, and reactively, so ...
The occurrence of the congestion has an extremely deleterious impact on the performance of Wireless Sensor Network (WSNs). ...
CONCLUSION The paper discourses Artificial Intelligence based techniques for Novel Conjunction Control in wireless sensor networks. ...
doi:10.34218/ijcet.10.2.2019.007
fatcat:pmmertuudjeehnez35btixqgci
Two-Step Path Loss Prediction by Artificial Neural Network for Wireless Service Area Planning
2019
IEICE Communications Express
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In this paper, we propose the two-step PL prediction method by the artificial neural network (ANN) to solve the issue. Firstly, the area is classified into several zones according to the PL range. ...
Our previous work showed that the path loss (PL) characteristics become complicated in the high PL region, and it can degrade the entire prediction accuracy. ...
In [1] [2], the path loss (PL) predictions in urban areas were investigated by the artificial neural networks (ANN). ...
doi:10.1587/comex.2019gcl0038
fatcat:dxxoz3fmpvgw5mbsqvl5aqs5ta
A Multipath Routing Algorithm Based on Traffic Prediction in Wireless Mesh Networks
2009
Communications and Network
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The technology of QoS routing has become a great challenge in Wireless Mesh Networks (WMNs). ...
MRATP consists of three modules including an algorithm on multipath routing built, a congestion discovery mechanism based on wavelet-neural network and a load balancing algorithm via multipath. ...
A new approach based on wavelet transform and artificial neural network is proposed for power system peak-load forecasting in literature [19] . ...
doi:10.4236/cn.2009.12013
fatcat:uaci5kgikrclbhhfcrqzevlh2a
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