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facilitate wireless broadcast or point-to-multipoint transmissions performance of a multi-tier drone network in terms of spectral efficiency of downlink transmission while illustrating the optimal intensity and altitude of drones in different tiers numerically
Poisson point process (PPP) drone small cell (DSC)
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Needs: high-rise urban: requires higher LoS connectivity suburban: a higher degree of path loss reduction
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QA higher altitude of drones -> higher LoS connectivity: reflection and shadowing are diminished lower altitude -> reduction in path loss
different altitudes for multi-tier drones -> an optimal trade-off between LoS connectivity and path loss
altitudes and locations
- Trajectory Planning And Mobility control Constraint: availability of the air corridors, connectivity, fuel limitation, collision, and terrain avoidance, regional borders, and military and civil aviation(altitude and coverage)
Methode: model the dynamics, position & velocity: adaptive communications
Altitude/location optimization, energy-efficiency optimization, intensity optimization optimal deployment: altitude and distance between UAVs
- Altitudes: depends on the statistical parameters of the underlying environment and path loss Shadowing and scattering caused by man-made structures -> free space path loss (FSPL) additional path loss has a Gaussian distribution closed-form expression to estimate the LoS probability between the UAV and the ground receiver : elevation angle and environment variables
optimal deployment: consider interference and interference-free situations to maximize the coverage.
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3D Placement: Revenue is proportional to the number of drone cell users To maximize the down-link sum-rate considering a network where both D2D transmitters and drone users are distributed as a PPP stop points for mobile UAVs
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Intensity:
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Traffic Off-loading/user Association model the cost as a function of coverage, capacity, delay, and achievable LoS Optimal Transport Theory (OTT)
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Power Minimization ensure minimum transmit power
A trade-off between LoS connectivity & path loss
- Investigate the feasibility of a multi-tier drone architecture over single-tier drones in a variety of urban environments.
Background: A terrestrial cellular network: BSs and users distributed as a homogeneous PPP with intensities lt and lu. Each BS transmits using a fixed-power Pt. A two-tier drone network where the big drones follow a 2D homogeneous PPP Fm with intensity l at a fixed altitude h. The small drones: a 2D homogeneous PPP Fs at an altitude hs with density ls. All big drones transmit with power Pm, small drones transmit at power Ps. Both tiers of drones and terrestrial BSs use the same spectrum for transmission. We consider the maximum received signal power-based association for the users. If multiple users are associated with the same BS, each user gets a channel with equal probability.
- Path-loss Model:
In most environments, a higher proportion of small drones is plausible. High-rise urban environment: a higher proportion of big drones need to be deployed: to provide higher LoS connectivity due to (their high altitude)
Reference [1] investigates the impact of altitude on the downlink coverage of a static UAV. The optimal UAV altitude is determined such that the power of the UAV can be minimized.
The authors in [10] formulate a 3D placement problem for UAVs to maximize the revenue of the network where revenue is proportional to the number of drone cell users
the drone altitude along with the coverage area. Optimal placement of drones is also studied in [12] in a network where drones assist the macrocells by serving users in times of congestion. A 3D placement algorithm is used to effectively serve the cellular users.
In [13], the authors study the optimal placement of cooperative UAVs with a target of minimizing network delays.
Reference [8] analyzes the performance for both static and dynamic UAVs.
The authors in [7] investigate the impact of spec- trum sharing in a drone small cell (DSC) net- work coexisting with the traditional cellular BSs where the DSCs are placed at a limited height.
Another interesting study is [6], where the authors consider a network of multiple UAVs with direc- tional antennas.
Another interesting study is [11] where a 3D placement problem for drones is formulated to serve a set of users in the downlink not covered by macrocells.
The authors in [7] study the UAV deployment problem focusing on traffic offloading in a het- erogeneous network composed of macro and small cells.
Using tools from stochastic geometry, in [14], the authors discuss an energy-efficient UAV deployment method to collect information from IoT devices in uplink.