Bandwidth, Power and Trajectory Optimization for UAV Base Station Networks With Backhaul and User QoS Constraints

Huang, Yingqian; Cui, Miao; Zhang, Guangchi; Chen, Wei

doi:10.1109/access.2020.2986075

Cited by 30 publications

(13 citation statements)

References 30 publications

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“…While there exists many works [21]- [25] that investigate throughput optimization problems. Specifically, the authors in [21] jointly optimized resource allocation and trajectory design to maximize system throughput.…”

Section: B Related Workmentioning

confidence: 99%

“…In [24], the authors maximized the minimum average throughput of all users in the downlink by jointly optimizing resource allocation as well as the UAV trajectory while satisfying users' delay-constrained. In [25], the authors maximized the minimum sum throughput of the delay-tolerant users by jointly optimizing the UAV trajectory, power allocation, and the bandwidth allocation of the backhaul link and the data links under guaranteeing the QoS of the delay-sensitive users. However, the works [24], [25] did not consider the influence of the time-varying characteristics of the wireless fading channel on the transmission delay.…”

Section: B Related Workmentioning

confidence: 99%

“…In [25], the authors maximized the minimum sum throughput of the delay-tolerant users by jointly optimizing the UAV trajectory, power allocation, and the bandwidth allocation of the backhaul link and the data links under guaranteeing the QoS of the delay-sensitive users. However, the works [24], [25] did not consider the influence of the time-varying characteristics of the wireless fading channel on the transmission delay. In [26], the authors maximized the effective capacity by optimizing power allocation under different statistical QoS constraints in downlink of a UAV-BS.…”

Section: B Related Workmentioning

confidence: 99%

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3D Location and Resource Allocation Optimization for UAV-Enabled Emergency Networks Under Statistical QoS Constraint

Niu

Zhao

Li³

2021

IEEE Access

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Unmanned aerial vehicles (UAVs) as aerial base stations have attracted great attention in emergency communication networks due to flexible deployment. With the popularization of smart devices, the demand for multimedia services is increasing in disaster relief. Therefore, it is very important to significantly improve throughput of unmanned aerial vehicle base station (UAV-BS) while ensuring qualityof-service (QoS) of multimedia traffic. In this paper, we consider a UAV-BS to serve a group of users in the downlink who have different statistical delay-bound QoS requirements in an emergency situation. We formulate a problem to maximize the sum statistical-QoS-guaranteed throughput (effective capacity) of all users by jointly optimizing the UAV's 3D location, power and bandwidth allocation under each user's statistical QoS requirement constraint. The formulated problem is a non-linear non-convex optimization problem, which is very difficult to solve. To this end, we propose an efficient iteration algorithm based on the block coordinate descent and successive convex optimization techniques to solve it. Specifically, we decouple the primary problem into three sub-problems, which can be approximated as easy-to-solve convex optimization problems. In each iteration, three sub-problems are alternately optimized. Finally, numerical simulation results prove the effectiveness of our proposed algorithm compared with benchmarks. INDEX TERMS UAV, unmanned aerial vehicle base station (UAV-BS), statistical QoS, successive convex optimization I. INTRODUCTION A. BACKGROUND AND MOTIVATIONC OMMUNICATION plays an indispensable role when the natural or man-made disasters occur. As the traditional communication network is based on the infrastructure, it will be damaged and cannot be rebuilt to restore communication in a short time [1], [2]. Besides, the smart phones of most ordinary users do not have the ability to directly connect to satellites. Recently, UAV is considered to be a promising technology to quickly provide wireless services because of its cost-effective, swiftly deployed , high mobility. [3], [4]. For this reason, more and more researches have begun to focus on integrating UAVs into emergency communication networks as aerial base stations [5], [6] or relays [7], [8]. However, some disasters occur in a large area and the communication facilities are severely damaged, such as earthquake and tsunami, the UAV cannot find a backhaul node on the ground to connect to the core network. Due to the low-earth orbit (LEO) satellite system operating in the space, so it hardly affected by the disaster. LEO satellite networks also have short transmission delay, large bandwidth and small path loss characteristics, etc [9]-[11]. Attracted by above reasons, LEO satellites are very suitable as backhaul nodes when lacking ground facilities [12].With the assistance of multimedia services, the rescue efficiency can be greatly improved. For example, the command center can obtain real-time video of the rescue scene, so as to issue accurate rescue...

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Section: B Related Workmentioning

confidence: 99%

Section: B Related Workmentioning

confidence: 99%

Section: B Related Workmentioning

confidence: 99%

See 1 more Smart Citation

3D Location and Resource Allocation Optimization for UAV-Enabled Emergency Networks Under Statistical QoS Constraint

Niu

Zhao

Li³

2021

IEEE Access

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“…In UAV-enabled emergency networks, besides energyefficient deployment, it is also very important to ensure the delay QoS requirement of each user. There are some previous research works [4,22,23] taking into account delay in UAV-enabled networks. The authors of [4] solved the minimum delay base station placement problem under constraints of UAV energy and speed.…”

mentioning

confidence: 99%

“…The authors of [4] solved the minimum delay base station placement problem under constraints of UAV energy and speed. In [22], on the premise of ensuring the QoS of delay-sensitive users, the capacity of delay-tolerant users was increased to maximize the throughput of the entire network by optimizing resource allocation and UAV's trajectory. In [23], the authors studied the optimal placement and distribution of cooperative UAVs to obtain the optimization of the overall network delays.…”

mentioning

confidence: 99%

Energy Efficiency Maximization for UAV‐Assisted Emergency Communication Networks

Niu

Zhao

Hou

et al. 2021

Wireless Communications and Mobile Computing

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Using unmanned aerial vehicles (UAVs) in emergency communications is a promising technology because of their flexible deployment, low cost, and high mobility. However, due to the limited energy of the onboard battery, the service duration of the UAV is greatly limited. In this paper, we study an emerging energy-efficient UAV emergency network, where a UAV works as an aerial base station to serve a group of users with different statistical quality-of-service (QoS) constraints in the downlink. In particular, the energy efficiency of the UAV is defined as the sum effective capacity of the downlink users divided by the energy consumption of the UAV, which includes the energy consumed by communication and the energy consumed by hovering. Then, we formulate an optimization problem to maximize the energy efficiency of the UAV by jointly optimizing the UAV’s altitude, downlink transmit power, and bandwidth allocation while meeting a statistical delay QoS requirement for each user. The formulated optimization problem is a nonlinear nonconvex optimization problem of fractional programming, which is difficult to solve. In order to deal with the nonconvex optimization problem, the following two steps are used. First, we transform the fractional objective function into a tractable subtractive function. Second, we decompose the original optimization problem into three subproblems, and then, we propose an efficient iterative algorithm to obtain the energy efficiency maximization value by using the Dinkelbach method, the block coordinate descent, and the successive convex optimization technique. Extensive simulation results show that our proposed algorithm has significant energy savings compared with a benchmark scheme.

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5G network slicing with unmanned aerial vehicles: Taxonomy, survey, and future directions

Bouzid

Chaib

Bensaad

et al. 2022

Trans Emerging Tel Tech

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With the ever‐expanding rise of network demands and user expectations, the fifth generation (5G) of cellular networks was envisioned to support a plethora of use cases and conflicting user demands. Next to providing traditional connectivity like its previous generations, 5G also promises to be a heterogeneous network connecting humans, vehicles, unmanned aerial vehicles (UAVs), smart devices, and more. These challenging expectations proved to be overwhelming for traditional network infrastructures to handle. Network slicing has emerged as a promising solution that can achieve such diverse, taxing, and sometimes conflicting requirements in a dynamic and programmable way. There is no denying that UAVs have attained significant focus and research in recent years, and with 5G already being deployed, UAVs can now exploit the capabilities of the new networks. Extensive research is being taken to integrate UAVs into networks, assisting and improving aspects like latency, coverage, and capacity. Motivated by these facts, this survey distinguishes itself from other works by jointly exploring 5G, network slicing, and UAVs. The main contributions of this article are to showcase how UAVs can assist networks, provide a taxonomy of UAVs in the context of network slicing, and survey works that contribute to network slicing with UAVs. In this article, we present a comprehensive survey on UAVs in the context of network slicing, covering contributions, and state‐of‐the‐art literature. We discuss network slicing in‐depth, focusing especially on the three major slices: enhanced Mobile BroadBand, massive machine type communications, and ultra‐reliable low‐latency communications. We provide an overview of 5G enablers, including software‐defined networking and network function virtualization. We cover UAVs and identify their roles in networks as both users and assistants. Furthermore, this survey provides insight into open issues and future research directions related to network slicing and UAVs before concluding.

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Bandwidth, Power and Trajectory Optimization for UAV Base Station Networks With Backhaul and User QoS Constraints

Cited by 30 publications

References 30 publications

3D Location and Resource Allocation Optimization for UAV-Enabled Emergency Networks Under Statistical QoS Constraint

3D Location and Resource Allocation Optimization for UAV-Enabled Emergency Networks Under Statistical QoS Constraint

Energy Efficiency Maximization for UAV‐Assisted Emergency Communication Networks

5G network slicing with unmanned aerial vehicles: Taxonomy, survey, and future directions

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