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We introduce the concept of using unmanned aerial vehicles (UAVs) as drone base stations for in-band Integrated Access and Backhaul (IB-IAB) scenarios for 5G networks. We first present a system model for forward link transmissions in an IB-IAB multi-tier drone cellular network. We then investigate the key challenges of this scenario and propose a framework that utilizes the flying capabilities of the UAVs as the main degree of freedom to find the optimal precoder design for the backhaul links, user-base station association, UAV 3D hovering locations, and power allocations. We discuss how the proposed algorithm can be utilized to optimize the network performance in both large and small scales. Finally, we use an exhaustive search-based solution to demonstrate the performance gains that can be achieved from the presented algorithm in terms of the received signal to interference plus noise ratio (SINR) and overall network sum-rate.
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The SINR Meta Distribution in Poisson Cellular Networks
This work studies the signal-to-interference-plus-noise ratio (SINR) meta distribution (MD) in cellular networks with a focus on the Poisson model. Firstly, we show that for stationary base station point processes, arbitrary fading, and power-law path loss with exponent α, the base station density λ and the noise power σ2 impact the SINR MD only through ηλα/2/σ2, termed the network signal-to-noise ratio (NSNR). Next, we show that for Poisson cellular networks, the SINR MD can be written as g(x)θ-2/α when the target SINR θ and the target reliability x jointly satisfy a constraint. We derive this constraint and the integral of g(x). Lastly, we discuss several extensions of the results to more general models and architectures.
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- Award ID(s):
- 2007498
- PAR ID:
- 10231798
- Date Published:
- Journal Name:
- IEEE Wireless Communications Letters
- ISSN:
- 2162-2337
- Page Range / eLocation ID:
- 1 to 1
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/LWC.2021.3068321
