LTE in the sky: trading off propagation benefits with interference costs for aerial nodes

By Bertold Van Der Bergh, Alessandro Chiumento, Sofie Pollin. IEEE Communications Magazine, 2016

309 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/ff74b6935cd793f1ab2a8029d9fc81e8bc7e065b

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In Aerospace Engineering

In Electrical and Electronic Engineering

Citations in the corpus

Common Throughput Maximization in UAV-Enabled OFDMA Systems With Delay Consideration (2018, background): “The measurement results in [26], [27] have shown that the air-to-ground communication channels are mainly dominated by the LoS links even when the UAV is at a moderate altitude.”
Cellular-Enabled UAV Communication: A Connectivity-Constrained Trajectory Optimization Perspective (2018, background): “It was shown in [14] that for a network with coexisting aerial and ground users, the performance of both types of users generally degrades as the aerial user altitude increases due to the more severe aerial interference.”
Ultra Reliable UAV Communication Using Altitude and Cooperation Diversity (2017, background): “However, as is reported in [11] and [28], the UAVs can be heavily affected by ground interferers due to the high probability of LoS.”
A Survey on Millimeter-Wave Beamforming Enabled UAV Communications and Networking (2021, background): “In fact, the measurement results in [134]–[136] have shown that A2G communication channels are mainly dominated by LoS links even if a UAV is located at a moderate altitude.”
Trajectory Design and Power Control for Multi-UAV Assisted Wireless Networks: A Machine Learning Approach (2018, background): “Before fully reaping all the aforementioned benefits, several technical challenges have to be tackled, including the optimal three dimensional (3D) deployment of UAVs, their interference management [15, 16], energy supply [9, 17], trajectory design [18], the channel model between the UAV and users [”
Cellular-Connected UAV: Potential, Challenges, and Promising Technologies (2018, unknown): “[2] B.”
What Will the Future of UAV Cellular Communications Be? A Flight From 5G to 6G (2021, methodology): “nals was determined exclusively by the BS antenna pattern [40], [41], [126].”
Interference Management for Cellular-Connected UAVs: A Deep Reinforcement Learning Approach (2018, background): “For instance, in [5], the authors study the impact of UAVs on the uplink performance of a ground LTE network.”
A Tutorial on UAVs for Wireless Networks: Applications, Challenges, and Open Problems (2018, background): “Moreover, the surveys in [14], [15], [31], [33], and [48]–[54] remain restricted to isolated UAV topics and use cases in wireless networking.”
Radio Channel Modeling for UAV Communication Over Cellular Networks (2017, methodology): “Measurements in LTE using a flying UAV were reported in [7] and results suggest there is a clearance of the radio path, obtained with higher UE heights, reduces the shadowing variation while it increases the received signal power from the interfering cells and the number of visible neighboring cells”
A Survey of Channel Modeling for UAV Communications (2018, methodology): “To this end, channel characterization with USRP hardware, such as N–210 [46], B–210 [48], X–310 [102], [103] and”
Accessing From the Sky: A Tutorial on UAV Communications for 5G and Beyond (2019, background): “In [23], measurements were taken with the main goal to quantify the interference experienced by aerial users at different altitudes.”