[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120438-en":3,"doc-seo-120438-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120438,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",6,"Technology","AirWave: Enhancing UAV Connectivity in Cellular Networks through an Integrated Simulation Framework","This study introduces AirWave, an integrated modeling framework for incorporating Unmanned Aerial Vehicles (UAVs) into 5G and future cellular networks. It combines network simulation and UAV physics realism by coupling ns-3 for network behavior with Gazebo for physical dynamics and integrating the PX4 autopilot for realistic flight dynamics. AirWave enables in-depth analysis of UAV mobility and handover management, including reinforcement learning–based trajectory optimization for cellular-connected drones. Large-scale simulations indicate improved UAV range, connectivity, and handover management performance, supporting both research progress and practical UAV communication applications.","2024 IEEE International Conference on Big Data & Machine Learning (ICBDML) | 979-8-3503-74 10-0/24/$31.00 ©2024 IEEE | DOI: 10. 1 109/ICBDML60909.2024. 10577299  \nAirWave: Enhancing UAV Connectivity in Cellular Networks through an Integrated Simulation  \nFramework  \nEmirhan Zor  \nElectronic and Communication Engineering Istanbul Technical University Istanbul, Turkey [emirhan.zor@hotmail.com](emirhan.zor@hotmail.com)  \nLeila Rzayeva  \nDept. of Intelligent Systems and Cybersecurity, Astana IT University, Astana, Kazakhstan [l.rzayeva@astanait.edu.kz](l.rzayeva@astanait.edu.kz)  \nYusuf Taskiran Electronic and Communication Engineering Istanbul Technical University Istanbul, Turkey [ytaskiran70@gmail.com](ytaskiran70@gmail.com)  \n[Zuleikha Syzdykova](Zuleikha Syzdykova), Dept. of Computer Engineering, Astana IT University, Astana, Kazakhstan, [Zuleikha.Syzdykova@astanait.edu.kz](Zuleikha.Syzdykova@astanait.edu.kz)  \nFares A. Dael Management Information Systems  \nİzmir Bakırçay University İzmir, Turkey [fares.dael@bakircay.edu.tr](fares.dael@bakircay.edu.tr)  \nIbraheem Shayea Electronic and Communication Engineering Istanbul Technical University Istanbul, Turkey[shayea@itu.edu.tr](shayea@itu.edu.tr)  \nAbstract— This study presents AirWave, an innovative modeling framework tailored to address the challenges associated with integrating Unmanned Aerial Vehicles (UAVs) into 5G and future cellular networks. With the rising prominence of UAVs across various industries, ensuring uninterrupted connectivity and efficient mobility management within cellular networks becomes paramount. AirWave offers a comprehensive simulation environment, amalgamating network and physics simulations using ns-3 for network simulation, Gazebo for physics simulation, and integrating PX4 autopilot for realistic UAV flight dynamics. This framework facilitates indepth investigations into UAV mobility and handover management, including a trajectory optimization technique based on reinforcement learning for cellular-connected drones. Extensive simulations demonstrate the versatility and effectiveness of AirWave, showcasing notable improvements in UAV range, connectivity, and management within cellular networks. These findings hold significant promise for advancing research and practical applications in UAV communications.  \nKeywords—UAV communications, 5G and beyond,Handover management, Reinforcement learning, machine learning.  \nI. INTRODUCTION  \nThe integration of Unmanned Aerial Vehicles (UAVs) into 5G and future cellular networks presents both promising opportunities and complex challenges. As highlighted by recent research [1], the role of UAVs as recipients and enablers of network services underscores the critical need for sophisticated mobility management and handover procedures, further enhanced by machine learning methods.  \nThe advent of 5G technology heralds transformative capabilities for mobile networks, offering ultra-low latency, expansive connectivity, and unprecedented data speeds essential for diverse applications. This evolution necessitates cutting-edge mobility and handover management systems to ensure seamless connectivity, particularly with the  \nproliferation of Internet of Things (IoT) devices and escalating data traffic [2] . Integrating 5G with UAV communication leverages advanced technologies such as Massive MIMO, mmWave, Beamforming, and NOMA to bolster efficiency and capacity [3], [4], [5] . Unlike LTE, 5G architecture employs network functions to minimize latency and optimize resource allocation, marking a significant shift in cellular network design.  \nEffective mobility management is paramount in 5G networks, particularly for UAVs, to ensure uninterrupted  \nFig. 1. Effective radiation pattern to terrestrial and drone users  \ncommunication between base stations [6], [7] . The Radio Resource Control (RRC) protocol plays a pivotal role in managing user equipment statuses, optimizing handovers, and resource allocation to reduce laten","cbCaipr9bzvF7l2w","https://ap.wps.com/l/cbCaipr9bzvF7l2w","pdf",4531147,1,8,"English","en",105,"# Introduction\n## UAV integration into 5G and cellular networks\n## Mobility management and handover challenges\n## UAV trajectory optimization and machine learning\n# AirWave framework overview","[{\"question\":\"What is AirWave, and what problem does it address?\",\"answer\":\"AirWave is an integrated modeling framework designed to handle challenges of integrating UAVs into 5G and future cellular networks, focusing on uninterrupted connectivity and mobility management.\"},{\"question\":\"How does AirWave combine simulation tools to model UAV behavior?\",\"answer\":\"AirWave uses ns-3 for network simulation, Gazebo for physics simulation, and integrates PX4 autopilot to produce realistic UAV flight dynamics.\"},{\"question\":\"What role does reinforcement learning play in AirWave?\",\"answer\":\"AirWave includes a reinforcement learning–based trajectory optimization technique to improve mobility and handover management for cellular-connected drones.\"}]","AirWave: Enhancing UAV Connectivity in Cellular Networks through an Integrated Simulation Framework | 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is AirWave, and what problem does it address?","Question",{"text":76,"@type":77},"AirWave is an integrated modeling framework designed to handle challenges of integrating UAVs into 5G and future cellular networks, focusing on uninterrupted connectivity and mobility management.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does AirWave combine simulation tools to model UAV behavior?",{"text":81,"@type":77},"AirWave uses ns-3 for network simulation, Gazebo for physics simulation, and integrates PX4 autopilot to produce realistic UAV flight dynamics.",{"name":83,"@type":74,"acceptedAnswer":84},"What role does reinforcement learning play in AirWave?",{"text":85,"@type":77},"AirWave includes a reinforcement learning–based trajectory optimization technique to improve mobility and handover management for cellular-connected 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