Modeling air-to-ground path loss for low altitude platforms in urban environments

By Akram Al-Hourani, Kandeepan Sithamparanathan, Abbas Jamalipour. 2014 IEEE Global Communications Conference, 2014

1,104 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/cd4ffdcd236e50454db2a86d43e63c44fb7a0c7c

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In Aerospace Engineering

In Electrical and Electronic Engineering

Citations in the corpus

A Survey of Channel Modeling for UAV Communications (2018, methodology): “In [9]–[11], environmental topography was realized with the statistical parameters recommended by the International”
Efficient 3-D placement of an aerial base station in next generation cellular networks (2016, background): “With the help of the results in [10], a channel model for air-to-ground communication in urban environments is presented in [6] and [11], and adopted here.”
Interference Management for Cellular-Connected UAVs: A Deep Reinforcement Learning Approach (2018, background): “The path loss between UAV j at location a and BS s, ξj,s,a, is given by [20]: ξj,s,a(dB) = 20 log10(dj,s,a) + 20 log10(f̂)− 147.”
UAV Trajectory Planning for Data Collection from Time-Constrained IoT Devices (2020, background): “The most widely A2G model is proposed in [8]; we refer the interested reader to [9] where different propagation models are investigated based on channel measurements.”
Wireless Communication Using Unmanned Aerial Vehicles (UAVs): Optimal Transport Theory for Hover Time Optimization (2017, methodology): “In our model, we consider a widely used probabilistic path loss model provided by International Telecommunication Union (ITU-R) [22], and the work in [5].”
Ultra Reliable UAV Communication Using Altitude and Cooperation Diversity (2017, background): “Moreover, the path loss modeling is based on free space conditions [14], making it impossible to address the effects of low altitudes which is of practical importance due to the regulation constraints.”
Energy Minimization for Wireless Communication With Rotary-Wing UAV (2018, background): “In this case, UAVs resemble the conventional static terrestrial BSs, but at a much higher altitude and thus possesses new channel characteristics [8]–[12].”
Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges (2018, neutral): “Therefore, more research is needed to study the use of such systems in providing communications coverage when the public communications network is disrupted or operates at its maximum capacity [24].”
Drone Small Cells in the Clouds: Design, Deployment and Performance Analysis (2014, methodology): “As discussed in [4] and [11], the ground receiver receives three groups of signals including LOS, strong reflected signals (NLOS) and multiple reflected components which cause multipath fading.”
Caching in the Sky: Proactive Deployment of Cache-Enabled Unmanned Aerial Vehicles for Optimized Quality-of-Experience (2016, background): “Clearly, the average path loss from the UAV k to user i at interval t is [13]:”
Optimal 3D-Trajectory Design and Resource Allocation for Solar-Powered UAV Communication Systems (2018, background): “In particular, channel coefficient hk[n] captures the shadowing and the small-scale fading effects due to multipath propagation [26], [27].”
LTE in the sky: trading off propagation benefits with interference costs for aerial nodes (2016, methodology): “[4] A.”
Survey on UAV Cellular Communications: Practical Aspects, Standardization Advancements, Regulation, and Security Challenges (2018, background): “A2G [50] 700MHz, 2000 MHz and 5800MHz 200m3000m Suburban, Urban, Dense and Highrise Urban MATLAB, Wireless InSite, One quasi-stationary UAV Finding a generic path loss model based on urban parameters”
A Survey on Millimeter-Wave Beamforming Enabled UAV Communications and Networking (2021, methodology): “The LoS probability is modeled as a logistic function of the elevation angle θ as follows [87], [114]”
Distributed Drone Base Station Positioning for Emergency Cellular Networks Using Reinforcement Learning (2018, methodology): “1 Manhattan grid urban layout whereas W and S are assumed equal across all buildings and are determined via [27] and”
Efficient Deployment of Multiple Unmanned Aerial Vehicles for Optimal Wireless Coverage (2016, methodology): “For the air-to-ground channel modeling, a common approach is to consider the LoS and non-line-ofsight (NLoS) links between the UAV and the ground users separately [9].”
Routing in Flying Ad Hoc Networks: A Comprehensive Survey (2020, background): “On the other hand, HAPs are relatively large and heavy, operate at a higher altitudes (altitude above 10 km), have longer ﬂying times, are quasi-stationary, and have wider coverage, but are relatively more costly and difﬁcult to deploy [37].”
Survey on Collaborative Smart Drones and Internet of Things for Improving Smartness of Smart Cities (2019, background): “The authors of [116] also discussed the prediction of path loss based on the urban environment.”
Optimal LAP Altitude for Maximum Coverage (2014, background): “3, 34) corresponding to Suburban, Urban, Dense Urban, and Highrise Urban respectively [4] (measured in dB).”