A new simple Unmanned Aerial Vehicle doppler effect RF reducing technique

Essaadali, Riadh; Kouki, Ammar B.

doi:10.1109/milcom.2016.7795490

Cited by 12 publications

(10 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Doppler shifts can introduce carrier frequency offset (CFO) and inter carrier interference, especially for orthogonal frequency division multiplexing (OFDM) implementations. There are several studies that consider modeling of Doppler spread [10], [22], [23], [30], [43]- [46]. Some channel access algorithms e.g., multi carrier code division multiple access, have been shown to be robust against Doppler spread in AG propagation [47].…”

Section: E Doppler Effectsmentioning

confidence: 99%

“…In such a case, if the receiver CFO synchronizer cannot mitigate the effect of these different frequency offsets, this results in intercarrier interference (ICI). In [23], a mitigation technique for Doppler shift was proposed for the case where the UAV is relaying between two communication nodes. The UAV acts as a repeater that provides the required frequency shift to mitigate the Doppler effect.…”

Section: Doppler Spreadmentioning

confidence: 99%

See 1 more Smart Citation

A Survey of Air-to-Ground Propagation Channel Modeling for Unmanned Aerial Vehicles

Khawaja

Güvenç

Matolak

et al. 2019

IEEE Commun. Surv. Tutorials

550

206

View full text Add to dashboard Cite

In recent years, there has been a dramatic increase in the use of unmanned aerial vehicles (UAVs), particularly for small UAVs, due to their affordable prices, ease of availability, and ease of operability. Existing and future applications of UAVs include remote surveillance and monitoring, relief operations, package delivery, and communication backhaul infrastructure. Additionally, UAVs are envisioned as an important component of 5G wireless technology and beyond. The unique application scenarios for UAVs necessitate accurate air-to-ground (AG) propagation channel models for designing and evaluating UAV communication links for control/non-payload as well as payload data transmissions. These AG propagation models have not been investigated in detail when compared to terrestrial propagation models. In this paper, a comprehensive survey is provided on available AG channel measurement campaigns, large and small scale fading channel models, their limitations, and future research directions for UAV communication scenarios.

show abstract

Section: E Doppler Effectsmentioning

confidence: 99%

Section: Doppler Spreadmentioning

confidence: 99%

A Survey of Air-to-Ground Propagation Channel Modeling for Unmanned Aerial Vehicles

Khawaja

Güvenç

Matolak

et al. 2019

IEEE Commun. Surv. Tutorials

550

206

View full text Add to dashboard Cite

show abstract

“…Consequently, by substituting the lower bound of 0.25 b n 2k + Φn Φn 2k can be obtained as (37), which finishes the proof.…”

Section: Subproblem 2: Uav Bandwidth and Transmit Power Optimizationmentioning

confidence: 60%

“…, where β 0 is the channel gain at a reference distance, i.e., d 0 = 1 meter, α is the path loss exponent, d n 1k denote the distance between the UAV and satellite at time slot n. Thanks to recent developing techniques, the Doppler shift at the satellite and UAV are assumed to be estimated and compensated accurately [37], [38]. We assume that the satellite starts from an initial point and moves along the x-axis with velocity v s [17].…”

Section: B Channel Modelmentioning

confidence: 99%

Satellite- and Cache-Assisted UAV: A Joint Cache Placement, Resource Allocation, and Trajectory Optimization for 6G Aerial Networks

Tran

Chatzinotas

Ottersten

2022

IEEE Open J. Veh. Technol.

View full text Add to dashboard Cite

This paper considers Low Earth Orbit (LEO) satellite-and cache-assisted unmanned aerial vehicle (UAV) communications for content delivery in terrestrial networks, which shows great potential for next-generation systems to provide ubiquitous connectivity and high capacity. Specifically, caching is provided by the UAV to reduce backhaul congestion, and the LEO satellite supports the UAV's backhaul link. In this context, we aim to maximize the minimum achievable throughput per ground user (GU) by jointly optimizing cache placement, the UAV's resource allocation, and trajectory while cache capacity and flight time are limited. The formulated problem is challenging to solve directly due to its non-convexity and combinatorial nature. To find a solution, the problem is decomposed into three sub-problems: (1) cache placement optimization with fixed UAV resources and trajectory, followed by (2) the UAV resources optimization with fixed cache placement vector and trajectory, and finally, (3) we optimize the UAV trajectory with fixed cache placement and UAV resources. Based on the solutions of sub-problems, an efficient alternating algorithm is proposed utilizing the block coordinate descent (BCD) and successive convex approximation (SCA) methods. Simulation results show that the max-min throughput and total achievable throughput enhancement can be achieved by applying our proposed algorithm instead of other benchmark schemes.

show abstract

“…, where β 0 is the channel gain at a reference distance, i.e., d 0 = 1 meter, α is the path loss exponent, d n 1k denote the distance between the UAV and satellite at time slot n. Thanks to recent developing techniques, the Doppler shift at the satellite and UAV are assumed to be estimated and compensated accurately [35], [36]. We assume that the satellite starts from an initial point and moves along the xaxis with velocity v s [16].…”

Section: B Channel Modelmentioning

confidence: 99%

Satellite- and Cache-assisted UAV: A Joint Cache Placement, Resource Allocation, and Trajectory Optimization for 6G Aerial Networks

Tran¹,

Chatzinotas²,

Ottersten³

2021

Preprint

View full text Add to dashboard Cite

This paper considers LEO satellite-and cacheassisted UAV communications for content delivery in terrestrial networks, which shows great potential for next-generation systems to provide ubiquitous connectivity and high capacity. Specifically, caching is provided by the UAV to reduce backhaul congestion, and the LEO satellite supports the UAV's backhaul link. In this context, we aim to maximize the minimum achievable throughput per ground user (GU) by jointly optimizing cache placement, the UAV's resource allocation, and trajectory while cache capacity and flight time are limited. The formulated problem is challenging to solve directly due to its non-convexity and combinatorial nature. To find a solution, the problem is decomposed into three sub-problems: (1) cache placement optimization with fixed UAV resources and trajectory, followed by ( 2) the UAV resources optimization with fixed cache placement vector and trajectory, and finally, (3) we optimize the UAV trajectory with fixed cache placement and UAV resources. Based on the solutions of sub-problems, an efficient alternating algorithm is proposed utilizing the block coordinate descent (BCD) and successive convex approximation (SCA) methods. Simulation results show that the max-min throughput and total achievable throughput enhancement can be achieved by applying our proposed algorithm instead of other benchmark schemes.

show abstract

A new simple Unmanned Aerial Vehicle doppler effect RF reducing technique

Cited by 12 publications

References 9 publications

A Survey of Air-to-Ground Propagation Channel Modeling for Unmanned Aerial Vehicles

A Survey of Air-to-Ground Propagation Channel Modeling for Unmanned Aerial Vehicles

Satellite- and Cache-Assisted UAV: A Joint Cache Placement, Resource Allocation, and Trajectory Optimization for 6G Aerial Networks

Satellite- and Cache-assisted UAV: A Joint Cache Placement, Resource Allocation, and Trajectory Optimization for 6G Aerial Networks

Contact Info

Product

Resources

About