Joint UAVs’ Load Balancing and UEs’ Data Rate Fairness Optimization by Diffusion UAV Deployment Algorithm in Multi-UAV Networks

Unmanned aerial vehicles (UAVs) can be deployed as base stations (BSs) for emergency communications of user equipments (UEs) in 5G/6G networks. In multi-UAV communication networks, UAVs’ load balancing and UEs’ data rate fairness are two challenging problems and can be optimized by UAV deployment st...

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Autores principales:	Zhirong Luan, Hongtao Jia, Ping Wang, Rong Jia, Badong Chen
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	UAV deployment load balancing virtual force field diffusion strategy success convex approximation Science Q Astrophysics QB460-466 Physics QC1-999
Acceso en línea:	https://doaj.org/article/f1f8d4710ccf42679c2485fbae3b0c4f
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id	oai:doaj.org-article:f1f8d4710ccf42679c2485fbae3b0c4f
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spelling	oai:doaj.org-article:f1f8d4710ccf42679c2485fbae3b0c4f2021-11-25T17:29:54ZJoint UAVs’ Load Balancing and UEs’ Data Rate Fairness Optimization by Diffusion UAV Deployment Algorithm in Multi-UAV Networks10.3390/e231114701099-4300https://doaj.org/article/f1f8d4710ccf42679c2485fbae3b0c4f2021-11-01T00:00:00Zhttps://www.mdpi.com/1099-4300/23/11/1470https://doaj.org/toc/1099-4300Unmanned aerial vehicles (UAVs) can be deployed as base stations (BSs) for emergency communications of user equipments (UEs) in 5G/6G networks. In multi-UAV communication networks, UAVs’ load balancing and UEs’ data rate fairness are two challenging problems and can be optimized by UAV deployment strategies. In this work, we found that these two problems are related by the same performance metric, which makes it possible to optimize the two problems simultaneously. To solve this joint optimization problem, we propose a UAV diffusion deployment algorithm based on the virtual force field method. Firstly, according to the unique performance metric, we define two new virtual forces, which are the UAV-UAV force and UE-UAV force defined by FU and FV, respectively. FV is the main contributor to load balancing and UEs’ data rate fairness, and FU contributes to fine tuning the UEs’ data rate fairness performance. Secondly, we propose a diffusion control stratedy to the update UAV-UAV force, which optimizes FV in a distributed manner. In this diffusion strategy, each UAV optimizes the local parameter by exchanging information with neighbor UAVs, which achieve global load balancing in a distributed manner. Thirdly, we adopt the successive convex optimization method to update FU, which is a non-convex problem. The resultant force of FV and FU is used to control the UAVs’ motion. Simulation results show that the proposed algorithm outperforms the baseline algorithm on UAVs’ load balancing and UEs’ data rate fairness.Zhirong LuanHongtao JiaPing WangRong JiaBadong ChenMDPI AGarticleUAV deploymentload balancingvirtual force fielddiffusion strategysuccess convex approximationScienceQAstrophysicsQB460-466PhysicsQC1-999ENEntropy, Vol 23, Iss 1470, p 1470 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	UAV deployment load balancing virtual force field diffusion strategy success convex approximation Science Q Astrophysics QB460-466 Physics QC1-999
spellingShingle	UAV deployment load balancing virtual force field diffusion strategy success convex approximation Science Q Astrophysics QB460-466 Physics QC1-999 Zhirong Luan Hongtao Jia Ping Wang Rong Jia Badong Chen Joint UAVs’ Load Balancing and UEs’ Data Rate Fairness Optimization by Diffusion UAV Deployment Algorithm in Multi-UAV Networks
description	Unmanned aerial vehicles (UAVs) can be deployed as base stations (BSs) for emergency communications of user equipments (UEs) in 5G/6G networks. In multi-UAV communication networks, UAVs’ load balancing and UEs’ data rate fairness are two challenging problems and can be optimized by UAV deployment strategies. In this work, we found that these two problems are related by the same performance metric, which makes it possible to optimize the two problems simultaneously. To solve this joint optimization problem, we propose a UAV diffusion deployment algorithm based on the virtual force field method. Firstly, according to the unique performance metric, we define two new virtual forces, which are the UAV-UAV force and UE-UAV force defined by FU and FV, respectively. FV is the main contributor to load balancing and UEs’ data rate fairness, and FU contributes to fine tuning the UEs’ data rate fairness performance. Secondly, we propose a diffusion control stratedy to the update UAV-UAV force, which optimizes FV in a distributed manner. In this diffusion strategy, each UAV optimizes the local parameter by exchanging information with neighbor UAVs, which achieve global load balancing in a distributed manner. Thirdly, we adopt the successive convex optimization method to update FU, which is a non-convex problem. The resultant force of FV and FU is used to control the UAVs’ motion. Simulation results show that the proposed algorithm outperforms the baseline algorithm on UAVs’ load balancing and UEs’ data rate fairness.
format	article
author	Zhirong Luan Hongtao Jia Ping Wang Rong Jia Badong Chen
author_facet	Zhirong Luan Hongtao Jia Ping Wang Rong Jia Badong Chen
author_sort	Zhirong Luan
title	Joint UAVs’ Load Balancing and UEs’ Data Rate Fairness Optimization by Diffusion UAV Deployment Algorithm in Multi-UAV Networks
title_short	Joint UAVs’ Load Balancing and UEs’ Data Rate Fairness Optimization by Diffusion UAV Deployment Algorithm in Multi-UAV Networks
title_full	Joint UAVs’ Load Balancing and UEs’ Data Rate Fairness Optimization by Diffusion UAV Deployment Algorithm in Multi-UAV Networks
title_fullStr	Joint UAVs’ Load Balancing and UEs’ Data Rate Fairness Optimization by Diffusion UAV Deployment Algorithm in Multi-UAV Networks
title_full_unstemmed	Joint UAVs’ Load Balancing and UEs’ Data Rate Fairness Optimization by Diffusion UAV Deployment Algorithm in Multi-UAV Networks
title_sort	joint uavs’ load balancing and ues’ data rate fairness optimization by diffusion uav deployment algorithm in multi-uav networks
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/f1f8d4710ccf42679c2485fbae3b0c4f
work_keys_str_mv	AT zhirongluan jointuavsloadbalancinganduesdataratefairnessoptimizationbydiffusionuavdeploymentalgorithminmultiuavnetworks AT hongtaojia jointuavsloadbalancinganduesdataratefairnessoptimizationbydiffusionuavdeploymentalgorithminmultiuavnetworks AT pingwang jointuavsloadbalancinganduesdataratefairnessoptimizationbydiffusionuavdeploymentalgorithminmultiuavnetworks AT rongjia jointuavsloadbalancinganduesdataratefairnessoptimizationbydiffusionuavdeploymentalgorithminmultiuavnetworks AT badongchen jointuavsloadbalancinganduesdataratefairnessoptimizationbydiffusionuavdeploymentalgorithminmultiuavnetworks
_version_	1718412322387525632

Joint UAVs’ Load Balancing and UEs’ Data Rate Fairness Optimization by Diffusion UAV Deployment Algorithm in Multi-UAV Networks

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