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Learning to Fly: A Distributed Deep Reinforcement Learning Framework for Software-Defined UAV Network Control

Hai Cheng, Lorenzo Bertizzolo, Salvatore DrOro, John Buczek, Tommaso Melodia, Elizabeth Serena Bentley
2021 IEEE Open Journal of the Communications Society  
To address these challenges, this article introduces a new architectural framework to control and optimize UAV networks based on Deep Reinforcement Learning (DRL).  ...  , and provides a scalable solution for large UAV networks.  ...  For these reasons, in this work, we select a datadriven approach and aim to solve the UAV network control problem through Deep Reinforcement Learning (DRL).  ... 
doi:10.1109/ojcoms.2021.3092690 fatcat:oq4zsqljhvdabi6jhrtyzdmpbu

Deep Learning and Reinforcement Learning for Autonomous Unmanned Aerial Systems: Roadmap for Theory to Deployment [article]

Jithin Jagannath, Anu Jagannath, Sean Furman, Tyler Gwin
2020 arXiv   pre-print
Then we discuss how reinforcement learning is explored for using this information to provide autonomous control and navigation for UAS.  ...  Therefore, in this chapter, we discuss how some of the advances in machine learning, specifically deep learning and reinforcement learning can be leveraged to develop next-generation autonomous UAS.  ...  The Aerostack software framework [117, 3, 116] defines an architectural design to enable advanced UAV autonomy.  ... 
arXiv:2009.03349v2 fatcat:5ylreoukrfcrtorzzp44mntjum

A Survey on Applications of Reinforcement Learning in Flying Ad-Hoc Networks

Sifat Rezwan, Wooyeol Choi
2021 Electronics  
Hence, many researchers have introduced reinforcement learning (RL) algorithms in FANETs to overcome these shortcomings.  ...  Flying ad-hoc networks (FANET) are one of the most important branches of wireless ad-hoc networks, consisting of multiple unmanned air vehicles (UAVs) performing assigned tasks and communicating with each  ...  DQL is a deep reinforcement learning (DRL) that works with -values similar to -learning, except for the -table part as shown in Figure 3 .  ... 
doi:10.3390/electronics10040449 fatcat:74luhksatzadplhluwzeszltuq

Reinforcement Learning to Optimize the Logistics Distribution Routes of Unmanned Aerial Vehicle [article]

Linfei Feng
2020 arXiv   pre-print
Based on the state-of-the-art Reinforcement Learning (RL), this paper proposed an improved method to achieve path planning for UAVs in complex surroundings: multiple no-fly zones.  ...  Comparing the model with the results obtained by the optimization solver OR-tools, it improves the reliability of the distribution system and has a guiding significance for the broad application of UAVs  ...  So we plan to use reinforcement learning to solve this problem.  ... 
arXiv:2004.09864v1 fatcat:twiyj4rrkzhn3pa4pot6lr4gbu

Flying Free: A Research Overview of Deep Learning in Drone Navigation Autonomy

Thomas Lee, Susan Mckeever, Jane Courtney
2021 Drones  
This work serves as a guide to research in drone autonomy with a particular focus on Deep Learning-based solutions, indicating key works and areas of opportunity for development of this area in the future  ...  Autonomy levels are cross-checked against the drone navigation tasks addressed in each work to provide a framework for understanding the trajectory of current research.  ...  .; Karimian, A.; Tron, R.; Dario, P. Aerial-DeepSearch: Distributed Multi-Agent Deep Reinforcement Learning for Search Missions.  ... 
doi:10.3390/drones5020052 fatcat:jqel25c655ajrkd2kzyyucy6ku

Survey on Q-Learning-Based Position-Aware Routing Protocols in Flying Ad Hoc Networks

Muhammad Morshed Alam, Sangman Moh
2022 Electronics  
A flying ad hoc network (FANETs), also known as a swarm of unmanned aerial vehicles (UAVs), can be deployed in a wide range of applications including surveillance, monitoring, and emergency communications  ...  Recently, owing to the advantages of multi-objective optimization, Q-learning (QL)-based position-aware routing protocols have improved the performance of routing in FANETs.  ...  Considering the high mobility, constraint energy, and memory resources of UAVs, the QL method is more suitable for FANET routing decision making than deep reinforcement learning because it is computationally  ... 
doi:10.3390/electronics11071099 fatcat:k76imusc65enrojpl3aqfsvg2e

AI'S Contribution to Ubiquitous Systems and Pervasive Networks Security – Reinforcement Learning vs Recurrent Networks

Christophe Feltus
2021 Journal of Ubiquitous Systems and Pervasive Networks  
Reinforcement learning and recurrent networks are two emerging machine-learning paradigms.  ...  In this paper, a systematic review of this research was performed in regard to various attacks and an analysis of the trends and future fields of interest for the RL and recurrent network-based research  ...  with a deep-Q network for dynamic route learning.  ... 
doi:10.5383/juspn.15.02.001 fatcat:tcfmazejvngihlmlqbt3gop72a

A Review on Communications Perspective of Flying Ad-Hoc Networks: Key Enabling Wireless Technologies, Applications, Challenges and Open Research Topics

Fazal Noor, Muhammad Asghar Khan, Ali Al-Zahrani, Insaf Ullah, Kawther A. Al-Dhlan
2020 Drones  
Thus, advancing to multiple small UAVs from a single large UAV has resulted in a new clan of networks known as flying ad-hoc networks (FANETs).  ...  Such networks provide reliability, ease of deployment, and relatively low operating costs by offering a robust communication network among the UAVs and base stations (BS).  ...  Deep Reinforcement Learning Since cellular technology is a key enabler for providing high-speed data communication services to the swarm of UAVs in the sky, however, it enforces challenges like supporting  ... 
doi:10.3390/drones4040065 fatcat:vk3txowlybggpetfitlcvd22ia

A Survey on Energy Optimization Techniques in UAV-Based Cellular Networks: From Conventional to Machine Learning Approaches [article]

Attai Ibrahim Abubakar, Iftikhar Ahmad, Kenechi G. Omeke, Metin Ozturk, Cihat Ozturk, Ali Makine Abdel-Salam, Michael S. Mollel, Qammer H. Abbasi, Sajjad Hussain, Muhammad Ali Imran
2022 arXiv   pre-print
The main idea is to deploy base stations on UAVs and operate them as flying base stations, thereby bringing additional capacity to where it is needed.  ...  In addition, for the purpose of completeness, we include a brief tutorial on the optimization methods and power supply and charging mechanisms of UAVs.  ...  His research interests include intelligent networking for wireless communication networks, with a focus on energy efficiency, mobility management, and radio resource management in cellular networks.  ... 
arXiv:2204.07967v1 fatcat:2x7dyojlvjfknibfsliiq4ozw4

Autonomous Unmanned Aerial Vehicle navigation using Reinforcement Learning: A systematic review

Fadi AlMahamid, Katarina Grolinger
2022 Engineering applications of artificial intelligence  
Autonomous UAV navigation is commonly accomplished using Reinforcement Learning (RL), where agents act as experts in a domain to navigate the environment while avoiding obstacles.  ...  Consequently, this study first identifies the main UAV navigation tasks and discusses navigation frameworks and simulation software.  ...  Deep Reinforcement Learning Deep Reinforcement Learning (DRL) uses deep agents to learn the optimal policy where it combines artificial Neural Networks (NN) with Reinforcement Learning (RL).  ... 
doi:10.1016/j.engappai.2022.105321 fatcat:j3ntdaycmzhbdeo4cqutgykwbu

A Review of Flying Ad Hoc Networks: Key Characteristics, Applications, and Wireless Technologies

Faezeh Pasandideh, João Paulo J. da Costa, Rafael Kunst, Nahina Islam, Wibowo Hardjawana, Edison Pignaton de Freitas
2022 Remote Sensing  
Recent advances in unmanned aerial vehicles (UAVs), or drones, have made them able to communicate and collaborate, forming flying ad hoc networks (FANETs).  ...  These interesting new avenues for the use of UAVs are motivating researchers to rethink the existing research on FANETs.  ...  An open-source platform for AI research AirSim [142] Simulator N/A Windows, Linux C++, C#, Python, Java experimentation, with computer vision, deep learning, and reinforcement learning algorithms for UAVs  ... 
doi:10.3390/rs14184459 fatcat:3cqpjgfj5bcrlebuxx3twx6hse

Artificial Intelligence for Autonomous Vehicular Communication Networks [From the Guest Editors]

Li-Chun Wang, Haris Gacanin, Dusit Niyato, Yu-Jia Chen, Chun-Hung Liu, Alagan Anpalagan
2022 IEEE Vehicular Technology Magazine  
vehicles are expected to play a vital role in a variety of areas. develop deep reinforcement learn- ing (DRL)-based solutions for au- tonomous vehicle-assisted MEC.  ...  The au- thors discuss fundamental tradeoffs between the arrangement delay and "Deep Reinforcement Learning- energy consumption involved in Based Resource Management for UAV flying and hovering.  ...  H i s research interests include cross-layer optimization for wireless systems, data-driven radio resource management, software-defined heterogeneous mobile networks, big data analysis for the Industrial  ... 
doi:10.1109/mvt.2022.3167907 fatcat:gjdiq2y5hjb45k7uinueuwz3ai

Table of Contents

2021 IEEE Open Journal of the Communications Society  
Saad Learning to Fly: A Distributed Deep Reinforcement Learning Framework for Software-Defined UAV Network Control H. Cheng, L. Bertizzolo, S. D'Oro, J. Buczek, T. Melodia, and E. S.  ...  Zhang An RFML Ecosystem: Considerations for the Application of Deep Learning to Spectrum Situational Awareness L. J. Wong, W. H. Clark, IV, B. Flowers, R. M. Buehrer, W. C. Headley, and A. J.  ... 
doi:10.1109/ojcoms.2022.3140882 fatcat:iqqbk24gezcdzpiliho73niz2i

Novel Multilayered Cellular Automata for Flying Cells Positioning on 5G cellular Self-Organising Networks

Evelin Helena Silva Cardoso, Jasmine Priscyla Leite De Araujo, Solon Venancio De Carvalho, Nandamudi Vijaykumar, Carlos Renato Lisboa Frances
2020 IEEE Access  
University of Pará (UFPA) for enabling the funding to enable the payment of publication fees.  ...  ACKNOWLEDGMENT The authors would like to thank the CAPES -Coordination for the Improvement of Higher Education -Finance Code 001 and PROPESP/UFPA -Dean of Research and Graduate Programs of the Federal  ...  Software-Defined Networking (SDN) is a relatively recent technology and features a prominent trait of a global vision that enables unified control over network entities.  ... 
doi:10.1109/access.2020.3045663 fatcat:pl4rsj7qnre77bcwektt7mxu4y

From 5G to 6G Technology: Meets Energy, Internet-of-Things and Machine Learning: A Survey

Mohammed Najah Mahdi, Abdul Rahim Ahmad, Qais Saif Qassim, Hayder Natiq, Mohammed Ahmed Subhi, Moamin Mahmoud
2021 Applied Sciences  
Due to the rapid development of the fifth-generation (5G) applications, and increased demand for even faster communication networks, we expected to witness the birth of a new 6G technology within the next  ...  The main contribution of this work is to provide a more comprehensive perspective, challenges, requirements, and context for potential work in the 6G communication standard.  ...  To create a tailored cellular IoT scheme with 5G URLLC protocol. [112] Software-Defined Security. Software-Defined Security.  ... 
doi:10.3390/app11178117 fatcat:4vtzn5cae5eqtnzobtvzysi6mm
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