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Artificial Neural Network based Path Loss Prediction for Wireless Communication Network

Lina Wu, Danping He, Bo Ai, Jian Wang, Hang Qi, Ke Guan, Zhangdui Zhong
2020 IEEE Access  
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

Quadri Ramon Adebowale, Nasir Faruk, Kayode S. Adewole, Abubakar Abdulkarim, Lukman A. Olawoyin, Abdulkarim A. Oloyede, Haruna Chiroma, Aliyu D. Usman, Carlos T. Calafate, Quanzhong Li
2021 Mobile Information Systems  
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]

Saud Aldossari, Kwang-Cheng Chen
2020 arXiv   pre-print
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

Finangwai D. Jacob, Deme C. Abraham, Gurumdimma Y. Nentawe
2020 Zenodo  
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: Paper Url :  ...  mobile communication network field strength prediction.  ... 
doi:10.5281/zenodo.3892767 fatcat:4lxone7fbndrbjwfyz7bqwr5la

AI-Empowered Propagation Prediction and Optimization for Reconfigurable Wireless Networks

Fusheng Zhu, Weiwen Cai, Zhigang Wang, Fang Li, Li Zhu
2022 Wireless Communications and Mobile Computing  
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

Harshita Tuli, Sanjay Kumar
2014 International Journal of Computer Applications  
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

Ismail Kirbas, Ayhan Dukkanci
2020 Figshare  
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

Mohamed K. Elmezughi, Omran Salih, Thomas J. Afullo, Kevin J. Duffy
2022 Sensors  
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]

Adeeb Salh, Lukman Audah, Qazwan Abdullah, Abdullah Noorsaliza, Nor Shahida Mohd Shah, Jameel Mukred, Shipun Hamzah
2021 arXiv   pre-print
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

Abdallah Mobark Aldosary, Saud Alhajaj Aldossari, Kwang-Cheng Chen, Ehab Mahmoud Mohamed, Ahmed Al-Saman
2021 Electronics  
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

V. M. Harnal, V. R. Budyal
2012 International Journal of Smart Sensor and Adhoc Network.  
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

Omar Ahmadien, Hasan F. Ates, Tuncer Baykas, Bahadir K. Gunturk
2020 IEEE Access  
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


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

Kentaro Saito, Yongri Jin, CheChia Kang, Jun-ichi Takada, Jenq-Shiou Leu
2019 IEICE Communications Express  
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

Zhiyuan LI, Ruchuan WANG
2009 Communications and Network  
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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