Learning radio maps for UAV-aided wireless networks: A segmented regression approach

Chen, Junting; Yatnalli, Uday; Gesbert, David

doi:10.1109/icc.2017.7997333

Cited by 137 publications

(84 citation statements)

References 6 publications

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“…The radio map thus offers useful information for various UAV placement or path planning designs. While the samples of power measurement in [174] for radio map construction were assumed to be given, they actually depend on the selected UAV trajectory during the learning phase. Therefore, the authors in [170] extended the work [174] by studying firstly the learning trajectory optimization problem to minimize the estimation error of channel model parameters, and then the communication trajectory design to maximize the communication throughput based on the learned channels.…”

Section: F Uav-assisted Communication Via Intelligent Learningmentioning

confidence: 99%

Accessing From the Sky: A Tutorial on UAV Communications for 5G and Beyond

2019

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Unmanned aerial vehicles (UAVs) have found numerous applications and are expected to bring fertile business opportunities in the next decade. Among various enabling technologies for UAVs, wireless communication is essential and has drawn significantly growing attention in recent years. Compared to the conventional terrestrial communications, UAVs' communications face new challenges due to their high altitude above the ground and great flexibility of movement in the threedimensional (3D) space. Several critical issues arise, including the line-of-sight (LoS) dominant UAV-ground channels and resultant strong aerial-terrestrial network interference, the distinct communication quality of service (QoS) requirements for UAV control messages versus payload data, the stringent constraints imposed by the size, weight and power (SWAP) limitations of UAVs, as well as the exploitation of the new design degree of freedom (DoF) brought by the highly controllable 3D UAV mobility. In this paper, we give a tutorial overview of the recent advances in UAV communications to address the above issues, with an emphasis on how to integrate UAVs into the forthcoming fifth-generation (5G) and future cellular networks. In particular, we partition our discussions into two promising research and application frameworks of UAV communications, namely UAVassisted wireless communications and cellular-connected UAVs, where UAVs serve as aerial communication platforms and users, respectively. Furthermore, we point out promising directions for future research and investigation. KeywordsUnmanned aerial vehicle (UAV), wireless communication, cellular network, channel model, antenna model, energy efficiency, air-ground interference, 3D placement and trajectory, optimization. TechnologyDescription Advantages Disadvantages Direct linkDirect point-to-point communication with ground node Simple, low cost Limited range, low data rate, vulnerable to interference, non-scalable Satellite Communication and Internet access via satellite Global coverage Costly, heavy/bulky/energyconsuming communication equipment, high latency, large signal attenuation Ad-hoc network Dynamically self-organizing and infrastructure-free network Robust and adaptable, support for high mobility Costly, low spectrum efficiency, intermittent connectivity, complex routing protocol Cellular network Enabling UAV communications by using cellular infrastructure and technologies Almost ubiquitous accessibility, cost-effective, superior performance and scalability Unavailable in remote areas, potential interference with terrestrial communications and high reliability/throughput for both air-to-air and airto-ground wireless communications in the three-dimensional (3D) space. Towards this end, four candidate communication technologies are listed and compared in Table III, including i) direct link; ii) satellite; iii) ad-hoc network; and iv) cellular network. In the following, we discuss the advantages as well as limitations of each of these technologies in detail.1) Direct Link: Due to its simpli...

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Section: F Uav-assisted Communication Via Intelligent Learningmentioning

confidence: 99%

Accessing From the Sky: A Tutorial on UAV Communications for 5G and Beyond

2019

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“…In this section, our goal is to find the UAV trajectory, over which the channel measurements are collected from the ground nodes, that results in the minimum estimation error of the channel model parameters. While the problem of learning the channel parameters from a pre-determined measurement data set has been addressed in the prior literature [16], [17], the novelty of our work lies in the concept of optimizing the flight trajectory itself so as to accelerate the learning process. The channel measurement collection and learning process are described next.…”

Section: Learning Trajectory Designmentioning

confidence: 99%

“…Assuming that the measurements collected over a trajectory are independent, the maximum likelihood estimation of ω s , s ∈ {LoS, NLoS} based on the measurements collected up to time step n is given by [16], [18] ω s,n = Ā T s,nĀs,n −1Ā T s,nḡs,n .…”

Section: A Measurement Collection and Channel Learningmentioning

confidence: 99%

Learning to Communicate in UAV-Aided Wireless Networks: Map-Based Approaches

Esrafilian

Gangula

Gesbert

2019

IEEE Internet Things J.

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We consider a scenario where an UAV-mounted flying base station is providing data communication services to a number of radio nodes spread over the ground. We focus on the problem of resource-constrained UAV trajectory design with (i) optimal channel parameters learning and (ii) optimal data throughput as key objectives, respectively. While the problem of throughput optimized trajectories has been addressed in prior works, the formulation of an optimized trajectory to efficiently discover the propagation parameters has not yet been addressed. When it comes to the communication phase, the advantage of this work comes from the exploitation of a 3D city map. Unfortunately, the communication trajectory design based on the raw map data leads to an intractable optimization problem. To solve this issue, we introduce a map compression method that allows us to tackle the problem with standard optimization tools. The trajectory optimization is then combined with a node scheduling algorithm. The advantages of the learning-optimized trajectory and of the map compression method are illustrated in the context of intelligent IoT data harvesting.

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“…Besides trajectory designs, researchers investigate the UAV deployment that provides the wireless coverage to the static users in a target geographical area, by designing the optimal operating location in three-dimensional (3D) airspace [9]-[11], minimizing the total deployment time [12], or minimizing the number of the stop points for the UAV [13].[14] and [15] study the economic issues, i.e., mechanism design and dynamic service pricing, in the multi-user UAV-aided network. In [16], the authors propose a machine learning approach to reconstruct a radio map of the air-to-ground channel across a dense urban environment, which is then exploited to search the global optimal UAV positioning for establishing the best wireless relay link between a BS and a static user in [17].Compared with static user networks, the UAV deployment design for random user networks is more challenging when the user locations are random.[18] proposes a machine learning approach to predict the user behaviors (i.e., content request distribution and mobility pattern) and designs the UAV deployment to meet the users' quality of experience requirement while minimizing the UAV's transmit power. In some other scenarios, the users' locations are highly random and unpredictable.…”

mentioning

confidence: 99%

Adaptive Deployment for UAV-Aided Communication Networks

Wang

Duan

Zhang

2019

IEEE Trans. Wireless Commun.

175

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Unmanned aerial vehicle (UAV) as an aerial base station is a promising technology to rapidly provide wireless connectivity to ground users. Given UAV's agility and mobility, a key question is how to adapt UAV deployment to best cater to the instantaneous wireless traffic in a territory. In this paper, we propose an adaptive deployment scheme for a UAV-aided communication network, where the UAV adapts its displacement direction and distance to serve randomly moving users' instantaneous traffic in the target cell. In our adaptive scheme, the UAV does not need to learn users' exact locations in real time, but chooses its displacement direction based on a simple majority rule by flying to the spatial sector with the greatest number of users in the cell. To balance the service qualities of the users in different sectors, we further optimize the UAV's displacement distance in the chosen sector to maximize the average throughput and the successful transmission probability, respectively. We prove that the optimal displacement distance for average throughput maximization decreases with the user density: the UAV moves to the center of the chosen sector when the user density is small and the UAV displacement becomes mild when the user density is large. In contrast, the optimal displacement distance for success probability maximization does not necessarily decrease with the user density and further depends on the target signal-to-noise ratio (SNR) threshold. Extensive simulations show that the proposed adaptive deployment scheme outperforms the traditional non-adaptive scheme, especially when the user density is not large. ). 1 I. INTRODUCTION Unmanned aerial vehicles (UAVs) aided wireless communication network is a promising solution to rapidly establish wireless connectivity for the mobile devices that are beyond the coverage of the terrestrial communication infrastructure [2]. For example, UAVs can work as mobile aerial base stations to provide emergency communication services to the ground terminals in battle fields, disaster scenes, congested roads, blind spots and rural areas. UAV-aided communication has two major advantages. First, with the agility and mobility features, a UAV can rapidly fly to serve the users closely and adapt to the on-demand surge. Second, compared with the traditional terrestrial base stations, the UAV operating at a high altitude connects to ground users via more reliable communication channels thanks to line-of-sight (LoS) links. A key design challenge of UAV-aided communication network is how to deploy a UAV to cater to wireless users' instantaneous traffic demands. Recent works have studied the UAV deployment for the static user networks, where the ground users' locations are fixed and known. For example, [3]-[7] design the trajectory of a single UAV to relay information [3], [4], broadcast/multicast messages [5], or provide offloading services to the ground users [6], [7], in the delay-tolerant systems. In [8], cooperative trajectory design for multiple UAVs is further investigated to mitiga...

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Learning radio maps for UAV-aided wireless networks: A segmented regression approach

Cited by 137 publications

References 6 publications

Accessing From the Sky: A Tutorial on UAV Communications for 5G and Beyond

Accessing From the Sky: A Tutorial on UAV Communications for 5G and Beyond

Learning to Communicate in UAV-Aided Wireless Networks: Map-Based Approaches

Adaptive Deployment for UAV-Aided Communication Networks

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