Literature DB >> 31779133

A Survey on Machine-Learning Techniques for UAV-Based Communications.

Petros S Bithas1, Emmanouel T Michailidis2, Nikolaos Nomikos3, Demosthenes Vouyioukas3, Athanasios G Kanatas4.   

Abstract

Unmanned aerial vehicles (UAVs) will be an integral part of the next generation wireless communication networks. Their adoption in various communication-based applications is expected to improve coverage and spectral efficiency, as compared to traditional ground-based solutions. However, this new degree of freedom that will be included in the network will also add new challenges. In this context, the machine-learning (ML) framework is expected to provide solutions for the various problems that have already been identified when UAVs are used for communication purposes. In this article, we provide a detailed survey of all relevant research works, in which ML techniques have been used on UAV-based communications for improving various design and functional aspects such as channel modeling, resource management, positioning, and security.

Entities:  

Keywords:  5G networks; air-to-ground communications; cellular networks; machine-learning; unmanned aerial vehicles (UAVs)

Year:  2019        PMID: 31779133      PMCID: PMC6929112          DOI: 10.3390/s19235170

Source DB:  PubMed          Journal:  Sensors (Basel)        ISSN: 1424-8220            Impact factor:   3.576


  4 in total

1.  Improved Random Forest for Classification.

Authors:  Angshuman Paul; Dipti Prasad Mukherjee; Prasun Das; Abhinandan Gangopadhyay; Appa Rao Chintha; Saurabh Kundu
Journal:  IEEE Trans Image Process       Date:  2018-05-10       Impact factor: 10.856

2.  Airborne Visual Detection and Tracking of Cooperative UAVs Exploiting Deep Learning.

Authors:  Roberto Opromolla; Giuseppe Inchingolo; Giancarmine Fasano
Journal:  Sensors (Basel)       Date:  2019-10-07       Impact factor: 3.576

3.  Automatic Modulation Classification Based on Deep Learning for Unmanned Aerial Vehicles.

Authors:  Duona Zhang; Wenrui Ding; Baochang Zhang; Chunyu Xie; Hongguang Li; Chunhui Liu; Jungong Han
Journal:  Sensors (Basel)       Date:  2018-03-20       Impact factor: 3.576

4.  Distributed Drone Base Station Positioning for Emergency Cellular Networks Using Reinforcement Learning.

Authors:  Paulo V Klaine; João P B Nadas; Richard D Souza; Muhammad A Imran
Journal:  Cognit Comput       Date:  2018-05-22       Impact factor: 5.418

  4 in total
  3 in total

Review 1.  A Systematic Review of Wi-Fi and Machine Learning Integration with Topic Modeling Techniques.

Authors:  Daniele Atzeni; Davide Bacciu; Daniele Mazzei; Giuseppe Prencipe
Journal:  Sensors (Basel)       Date:  2022-06-29       Impact factor: 3.847

Review 2.  Review of Protocol Stack Development of Flying Ad-hoc Networks for Disaster Monitoring Applications.

Authors:  Ruchi Dhall; Sarang Dhongdi
Journal:  Arch Comput Methods Eng       Date:  2022-07-27       Impact factor: 8.171

3.  A Comprehensive Collection and Analysis Model for the Drone Forensics Field.

Authors:  Fahad Mazaed Alotaibi; Arafat Al-Dhaqm; Yasser D Al-Otaibi; Abdulrahman A Alsewari
Journal:  Sensors (Basel)       Date:  2022-08-29       Impact factor: 3.847

  3 in total

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