QoS-Compliant 3-D Deployment Optimization Strategy for UAV Base Stations

Zhong, Xukai; Huo, Yiming; Dong, Xiaodai; Liang, Zhonghua

doi:10.1109/jsyst.2020.3015428

Cited by 57 publications

(27 citation statements)

References 42 publications

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“…In the simulations we considered a centralized decision making for UAVs positioning and trajectory. Referring to [20] and [21], as mentioned in Table V we consider a 5000 × 5000 meter urban area with scenarios including 80, 200, and 450 users with Poison Point Process (PPP) distribution. The PPP parameter λ = 20000, and the environment parameters are as follows: f c = 2 GHz, P L max = 110 dB, and (a, b, δ LoS , δ N LoS ) = (9.61, 0.43, 0.1, 20) corresponding to urban environments.…”

Section: A Test Systemmentioning

confidence: 99%

Managing Sets of Flying Base Stations Using Energy Efficient 3D Trajectory Planning in Cellular Networks

Sobouti¹,

Mohajerzadeh²,

Seno³

et al. 2022

Preprint

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Section: A Test Systemmentioning

confidence: 99%

Managing Sets of Flying Base Stations Using Energy Efficient 3D Trajectory Planning in Cellular Networks

Sobouti¹,

Mohajerzadeh²,

Seno³

et al. 2022

Preprint

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Section: Related Workmentioning

confidence: 99%

“…Heuristic based approach can provide a feasible way to tackle intricate issues such as paper [14], [15]. Paper [14] proposed a novel genetic algorithm-based 2D placement approach to maximum coverage with consideration of data rate distribution, its drawback is only feasible to 2D movement. Paper [15] took usage of particle swarm optimization (PSO) to adjust flight altitude and beam angletomaximizing the coverage under the constraint of transmitting power, however, the mobility of UAVs was not taken into consideration.…”

Section: Related Workmentioning

confidence: 99%

“…Despite of the extensive researches and applications in UAV assisted wireless communication networks, few literature refers to the UAVs placement, channel allocation as well as UEs scheduling with the comprehensively consideration of system QoE requirement such as delay, throughput and energy efficiency. Besides, in literatures like [14], [22], the height of UAVs was fixed in the papers above. Actually, in 3D space, UAVs always tend to fly close to UEs for a better communication quality not only in the horizontal distance but also in the height.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Joint Power and QoE Optimization Scheme for Multi-UAV Assisted Offloading in Mobile Computing

Wang

Gao

2021

IEEE Access

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Recent years, unmanned aerial vehicles (UAVs) have attracted much attention for providing intermediate relay to ground mobile user equipments (UEs) for their flexible mobility. UEs can offload computing-intensive task to mobile cloud computing (MCC) or mobile edge computing (MEC) for fast processing. However, with multi-UAV and ground mobile UEs in the system, heterogeneous performance requirement as well as fast-changing communication condition make the system more complicated. Meanwhile, both UEs and UAVs are battery-driven. How to optimize the energy efficiency for UEs' transmission and UAVs' position should be carefully considered. Since this is a non-convex and mixedinteger optimization problem, a heuristic joint power and quality of experience (HJPQ) algorithm is proposed in this paper, where the UEs' offloading delay, MIMO channel, transmission power, as well as UAVs' placement are jointly optimized. The numeral simulations not only reveal the effectiveness of HJPQ, but also guarantee the great quality of experience (QoE) performance for UEs with different priorities. Furthermore, the comparison experiments with random assignment and deep deterministic policy gradient (DDPG) show the superiority of HJPQ in lower complexity, faster convergence, shorter offloading delay as well as higher energy efficiency.

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“…Researchers have already started investigating various possible techniques, such as highfrequency bands, for example, millimeter wave (mmWave), sub-terahertz, and terahertz (THz) [16], advanced computing technologies, for example, central cloud computing and mobile edge computing (MEC), and security-related technologies, such as blockchain, for the successful implementation of 6G communications in 2030 [17][18]. The 6G communication systems are being motivated to address several critical issues, such as ultra-high reliability, high energy efficiency (EE) [19][20], ultra-low end-to-end (E2E) latency, ultra-high network availability, and communications convergence. Specifically, three novel IoT applications and services are intended for 6G communications, incorporating contextually agile eMBB communications (CAeC), event-defined uRLLC (EDuRLLC), and computation-oriented communications (COC).…”

Section: Introductionmentioning

confidence: 99%

Distributed Learning for 6G–IoT Networks: A Comprehensive Survey

Das¹,

Mudi²,

Rahman³

et al. 2022

Preprint

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<p>Smart services based on the Internet of Things (IoT) are likely to grow in popularity in the forthcoming years, necessitating the improvement of fifth-generation (5G) cellular networks upgrade of future networks from their present state. Despite the fact that the 5G cellular networks may manage a diversity of IoT services, they may not be able to fully meet the requirements of emerging smart applications due to their limitations that, in many cases, could be overcome by applying artificial intelligence (AI). Therefore, sixth–generation (6G) wireless technologies are being developed to address the limitations of 5G networks. Traditional machine learning (ML) techniques are driven in a centralized way. However, the huge volume of produced wireless data, the confidentiality concerns, and the growing computing competencies of wireless edge devices have led to the exposure of a promising solution in a decentralized way which is called distributed learning. This paper provides a comprehensive analysis of distributed learning (e.g., federated learning (FL), multi–agent reinforcement learning (MARL)–based FL framework) and how to deploy in an effective and efficient way for wireless networks. Moreover, we describe a timely comprehensive review of the role of FL in facilitating 6G enabling technologies, such as mobile edge computing, network slicing, satellite communications, terahertz links, blockchain, and semantic communications. Also, we identify and discuss several open research issues related to FL–empowered 6G wireless networks. In particular, we focus on FL for enabling an extensive range of smart services and applications. For each application, the main motivation for using FL along with the associated challenges and detailed examples for use scenarios are given. Regarding the AI techniques, we consider MARL–based FL framework tailored to the needs of future wireless networks for ensuring fast convergence and high model accuracy of large state and action spaces. Particularly, to manage the fast varying radio channels and limited radio resources (e.g., transmission power and radio spectrum) in a cellular communication environment, this article proposes a robust MARL–based FL framework to enable local users to perform distributed power allocation, mode selection, resource allocation, and interference management. Finally, the paper outlines several prospective upcoming research topics, aimed to create constructive incorporation of MARL–based FL framework for future wireless networks.</p>

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QoS-Compliant 3-D Deployment Optimization Strategy for UAV Base Stations

Cited by 57 publications

References 42 publications

Managing Sets of Flying Base Stations Using Energy Efficient 3D Trajectory Planning in Cellular Networks

Managing Sets of Flying Base Stations Using Energy Efficient 3D Trajectory Planning in Cellular Networks

Joint Power and QoE Optimization Scheme for Multi-UAV Assisted Offloading in Mobile Computing

Distributed Learning for 6G–IoT Networks: A Comprehensive Survey

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