Interference Avoidance Position Planning in Dual-Hop and Multi-Hop UAV Relay Networks

Hosseinalipour, Seyyedali; Rahmati, Ali; Dai, Huaiyu

doi:10.1109/twc.2020.3007766

Cited by 44 publications

(22 citation statements)

References 40 publications

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“…In this work, the UAV adjusts its heights in order to improve the probability of coverage. Dual-hop or multiple hop communication in a UAV relaying system is addressed in [ 33 , 34 ]. The average end-to-end throughput between a pair of fixed transmitter and receiver with the impact of interference in the environment are optimized in these works.…”

Section: Related Workmentioning

confidence: 99%

Deep Q-Learning for Two-Hop Communications of Drone Base Stations

Fotouhi

Ding

Hassan

2021

Sensors

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In this paper, we address the application of the flying Drone Base Stations (DBS) in order to improve the network performance. Given the high degrees of freedom of a DBS, it can change its position and adapt its trajectory according to the users movements and the target environment. A two-hop communication model, between an end-user and a macrocell through a DBS, is studied in this work. We propose Q-learning and Deep Q-learning based solutions to optimize the drone’s trajectory. Simulation results show that, by employing our proposed models, the drone can autonomously fly and adapts its mobility according to the users’ movements. Additionally, the Deep Q-learning model outperforms the Q-learning model and can be applied in more complex environments.

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Section: Related Workmentioning

confidence: 99%

Deep Q-Learning for Two-Hop Communications of Drone Base Stations

Fotouhi

Ding

Hassan

2021

Sensors

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“…, and q m [n] − q k [n] in (27b), (27e), (27f), and (27g), respectively, with their respective lower bounds given in (28), (31), (32), and (37), respectively, we formulate an approximation to (27) [n]) in (38f) are concave with respect to Q, and the right hand side of (38g) is linear with respect to Q. Therefore, problem (38) is convex and can be solved optimally by the interior-point method [32].…”

Section: B Sub-problem 2: Optimization Of Q Given (A P)mentioning

confidence: 99%

“…In [27], network reconstruction using UAV ad hoc networks has been investigated, and the network performance has been optimized with optimal UAV deployment. In [28], a multi-UAV relaying system in an interference environment has been considered, where the optimal UAV relay number and the optimal UAV relay positioning have been optimized to maximize the throughput. In [29], a new on-demand wireless backhaul scheme based on multiple UAV relays has been proposed, which enables UAVs to form a multi-hop backhaul network in a decentralized manner.…”

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confidence: 99%

Throughput Improvement for Multi-Hop UAV Relaying

et al. 2019

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Unmanned aerial vehicle (UAV) relaying is one of the main technologies for UAV communications. It uses UAVs as relays in the sky to provide reliable wireless connection between remote users. In this paper, we consider a multi-hop UAV relaying system. To improve the spectrum efficiency of the system, we maximize the average end-to-end throughput from the source to the destination by jointly optimizing the bandwidth allocated to each hop, the transmit power for the source and relays, and the trajectories of the UAVs, subject to constraints on the total spectrum bandwidth, the average and peak transmit power, the UAV mobility and collision avoidance, and the information-causality of multi-hop relaying. The formulated optimization is non-convex. We propose an efficient algorithm to approximate and solve it, using the alternating optimization and successive convex optimization methods. Numerical results show that the proposed optimization significantly outperforms other benchmark schemes, verifying the effectiveness of our scheme. INDEX TERMS Bandwidth allocation, multi-hop relaying, power allocation, trajectory optimization, UAV communication. I. INTRODUCTION Due to the advantages of high flexibility, low cost, and ease of use, unmanned aerial vehicles (UAVs) have been widely used in cargo delivery, traffic monitoring, precision agriculture, and so on. Recently, research has shown that integrating UAVs into communication systems is an effective way to improve wireless performance and it has the following advantages [1], [2]. First, on-demand UAV wireless communication system can be swiftly deployed with low cost for temporary and emergent coverage. Second, lineof-sight (LoS) links can be established between UAVs and ground nodes or between different UAVs with high probability, which can improve the link quality of the system. Third, the communication performance of a UAV wireless communication system can be improved by adaptive resource allocation along with UAV trajectory design. On the other The associate editor coordinating the review of this manuscript and approving it for publication was Shravana Musunuri.

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“…One studies the optimization of UAV trajectory and position, and air-to-ground channel models in recent years and tries to integrate UAVs into 5G communication systems to enhance the system performance by utilizing some unique characteristics of UAVs, such as the high line of sight (LoS) links' possibility [1]. Moreover, in the literature, UAVs have been widely applied as base stations [2], [15], [16], [19]- [22], [28], [29], relays [3], [17], [18], [24]- [27], wireless chargers [4], data collectors/disseminators [5], [23], and mobile edge computing (MEC) servers [6].…”

Section: Introductionmentioning

confidence: 99%

“…By optimizing the locations of UAVs and users' powers, it maximizes the sum rate of users. In [24], there are some interference sources in the environment, and it aims to minimize the number of UAVs to serve a pair of users while guaranteeing the desired signal-to-interference ratio of this system by optimizing the UAVs' locations. Reference [25] optimizes the positions of UAVs and resource allocations together to maximize the system throughput in a UAV relaying system, where multiple UAVs aim to relay information from a gateway node to ground users.…”

Section: Introductionmentioning

confidence: 99%

Joint Position and Resource Optimization for Multi-UAV-Aided Relaying Systems

Chen

2020

IEEE Access

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Unmanned aerial vehicles (UAVs) can enhance the overall performance of terrestrial communication systems due to their high possibility of the line of sight (LoS) links and on-demand deployments. The advantages of the UAV position optimization now attract attentions from researchers to study the cooperation among static UAV relays. In this paper, we study a relaying system, where multiple UAVs establish a cooperative UAV relay network to help some transmitters (Txs) communicate with their corresponding receivers (Rxs) by employing orthogonal frequency division multiple access (OFDMA). We maximize the paired Tx-Rxs' minimum rate through solving a non-convex, information causality constraints involved problem by optimizing the UAV relays' locations, nodes' powers, and bandwidth allocations together. An iterative algorithm is proposed by using block coordinate descent (BCD) and successive convex approximation (SCA) methods. Moreover, we also propose a new position initialization method, discuss some special cases of our problem to show some insights for future works, and prove the convergence of our algorithm. Simulations show the effectiveness of our proposed algorithm, some interesting trade-offs about the optimized UAVs' locations, and the influence of available resources.

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Interference Avoidance Position Planning in Dual-Hop and Multi-Hop UAV Relay Networks

Cited by 44 publications

References 40 publications

Deep Q-Learning for Two-Hop Communications of Drone Base Stations

Deep Q-Learning for Two-Hop Communications of Drone Base Stations

Throughput Improvement for Multi-Hop UAV Relaying

Joint Position and Resource Optimization for Multi-UAV-Aided Relaying Systems

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