Learning in the Sky: Towards Efficient 3D Placement of UAVs

Arani, Atefeh Hajijamali; Azari, Mohammad Mahdi; Melek, William; Safavi‐Naeini, Safieddin

doi:10.1109/pimrc48278.2020.9217381

Cited by 20 publications

(13 citation statements)

References 19 publications

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“…To improve the throughput in overloaded and outage situations, an RL-based ABS 3D deployment approach was proposed in [17], where UAVs found their optimal location to increase system performance gain. Similarly, 3D ABS deployment was presented in [18] to maximize users' coverage by finding ABS optimal altitude and location using a bisection search algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…The optimum altitude can be obtained on the basis of the number of UAVs and beam width of directional antennas [12] UAV-aided cellular communication network against jamming Reinforcement learning A minimized bit error rate and energy saving for the cellular network [13] Single UAV to provide wireless coverage for indoor users when cellular network goes down Gradient descent algorithm A minimum transmit power with maximum path loss was obtained [14] Optimizing the height of a UAV to maximize coverage and minimizing outage probability Decode and forward-relaying method Maximum coverage with minimum outage was obtained by finding the optimum height of a UAV [17] A 3D deployment of UAV to improve throughput in overloaded and outage situations…”

Section: Circle-packing Theorymentioning

confidence: 99%

See 1 more Smart Citation

Prioritized User Association for Sum-Rate Maximization in UAV-Assisted Emergency Communication: A Reinforcement Learning Approach

Siddiqui

Aqeel

Alkhayyat

et al. 2022

Drones

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Unmanned air vehicles (UAVs) used as aerial base stations (ABSs) can provide communication services in areas where cellular network is not functional due to a calamity. ABSs provide high coverage and high data rates to the user because of the advantage of a high altitude. ABSs can be static or mobile; they can adjust their position according to real-time location of ground user and maintain a good line-of-sight link with ground users. In this paper, a reinforcement learning framework is proposed to maximize the number of served users by optimizing the ABS 3D location and power. We also design a reward function that prioritize the emergency users to establish a connection with the ABS using Q-learning. Simulation results reveal that the proposed scheme clearly outperforms the baseline schemes.

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Section: Related Workmentioning

confidence: 99%

Section: Circle-packing Theorymentioning

confidence: 99%

Prioritized User Association for Sum-Rate Maximization in UAV-Assisted Emergency Communication: A Reinforcement Learning Approach

Siddiqui

Aqeel

Alkhayyat

et al. 2022

Drones

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“…The high propagation delay of satellite systems are the major barrier in using them for uRLLC purpose, however, the UAVs can still remarkably contribute into this aspect of 5G use cases too. For such purposes, UAVs acting as aerial BSs (see Figure (1d)) are flexible platforms that can adjust their 3D locations and employ 5G communication technologies to serve the target users [57], [58]. Finally, [7] provides an overview of the 3GPP NTN features, uncovering their potential to satisfy consumer expectations in 5G networks.…”

Section: B Key Drivers For Ntns Integration In 5gmentioning

confidence: 99%

Evolution of Non-Terrestrial Networks From 5G to 6G: A Survey

Solanki¹,

Chatzinotas²,

Kodheli³

et al. 2021

Preprint

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Non-terrestrial networks (NTNs) traditionally had certain limited applications. However, the recent technological advancements opened up myriad applications of NTNs for 5G and beyond networks, especially when integrated into terrestrial networks (TNs). This article comprehensively surveys the evolution of NTNs highlighting its relevance to 5G networks and essentially, how it will play a pivotal role for the development of 6G and beyond wireless networks. The survey discusses important features of NTNs integration into TNs by delving into the new range of services and use cases, various architectures, and new approaches being adopted to develop new wireless ecosystem. Our survey includes the major progresses and outcomes from academic research as well as industrial efforts. We first start with introducing the relevant 5G use cases and general integration challenges such as handover and deployment difficulties. Then, we review the NTNs operations in mmWave and their potential for internet of things (IoT). Further, we discuss the significance of mobile edge computing (MEC) and machine learning (ML) in NTNs by reviewing the relevant research works. Furthermore, we also discuss the corresponding higher layer advancements and relevant field trials/prototyping at both academic and industrial levels. Finally, we identify and review 6G and beyond application scenarios, novel architectures, technological enablers, and higher layer aspects pertinent to NTNs integration.

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“…In [13], the coverage area for UAVs in the presence of co-channel interference was maximized. In [14], a set of UAVs were deployed to assist a macro BS (MBS) in downlink for overload situations. To solve the problem in a distributed manner, reinforcement learning algorithms were proposed.…”

Section: Introductionmentioning

confidence: 99%

Fairness-Aware Link Optimization for Space-Terrestrial Integrated Networks: A Reinforcement Learning Framework

Arani

Zhu

2021

IEEE Access

Self Cite

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The integration of space and air components considering satellites and unmanned aerial vehicles (UAVs) into terrestrial networks in a space-terrestrial integrated network (STIN) has been envisioned as a promising solution to enhancing the terrestrial networks in terms of fairness, performance, and network resilience. However, employing UAVs introduces some key challenges, among which backhaul connectivity, resource management, and efficient three-dimensional (3D) trajectory designs of UAVs are very crucial. In this paper, low-Earth orbit (LEO) satellites are employed to alleviate the backhaul connectivity issues with UAV networks, where we address the problem of jointly determining backhaul-aware 3D trajectories of UAVs, resource management, and associations between users, satellites and base stations (BSs) in an STIN while satisfying ground users' quality-of-experience requirements and provisioning fairness concerning users' data rates. The proposed approach maximizes a novel objective function with joint consideration for BS's load and fairness, which can be categorized as a non-deterministic polynomial time hard (NPhard) problem. To tackle this issue, we leverage a reinforcement learning framework, in which our problem is modeled as a multi-armed bandit problem. Accordingly, BSs learn the environment and its dynamics and then make decisions under an upper confidence bound based method. Simulation results show that our proposed approach outperforms the benchmark methods in terms of fairness, throughput, and load.

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Learning in the Sky: Towards Efficient 3D Placement of UAVs

Cited by 20 publications

References 19 publications

Prioritized User Association for Sum-Rate Maximization in UAV-Assisted Emergency Communication: A Reinforcement Learning Approach

Prioritized User Association for Sum-Rate Maximization in UAV-Assisted Emergency Communication: A Reinforcement Learning Approach

Evolution of Non-Terrestrial Networks From 5G to 6G: A Survey

Fairness-Aware Link Optimization for Space-Terrestrial Integrated Networks: A Reinforcement Learning Framework

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