Multi-Tier Variable Height UAV Networks: User Coverage and Throughput Optimization

Abstract

Unmanned aerial vehicles (UAVs) are increasingly considered to act as base stations (BSs) for the future wireless networks. Some of the crucial UAV-assisted network design challenges are the network coverage, throughput, and energy efficiency. Therefore, fast, low-complexity, and efficient UAV placement and resource allocation strategies are imperative. This paper presents a novel variable height multi-UAV deployment strategy to exploit the 3D flexibility of UAVs as BSs. We propose a multi-tier variable height UAV-based network deployment and compare its performance with the state-of-the-art equal height deployment. Height optimization is performed to deliver energy efficiency and throughput maximization for each cell. The results show that our proposed method is more energy-efficient in a multicell UAV network than the most widely used height optimization method in the literature. In UAV networks, users at the cell edges can receive very poor signal-to-interference-plus-noise ratio (SINR) levels due to interfering UAVs. To cope with this problem, we adopt a fractional frequency reuse (FFR) scheme to compensate low SINR levels. We optimize the SINR threshold corresponding to each cell to maximize their spectral efficiency (SE), thereby improving the network's area spectral efficiency (ASE). The numerical results show that the proposed deployments provide significant gains in coverage density, SINR coverage probability, rate coverage, and ASE compared to equal height benchmark scheme. As the number of UAVs increases, the number of tiers need to increase to preserve the rate coverage of the network. Moreover, the performance of the proposed variable height model is expected to converge to that of equal height cellular design for a large number of UAVs.

Resource Scheduling Based on Deep Reinforcement Learning in UAV Assisted Emergency Communication Networks

“…Their approach uses a joint power allocation and UAV altitude control scheme to maximize the uplink throughput of the UAV network. Meanwhile, other researchers consider the optimization of user coverage and throughput in multi-tier variable height UAV networks [39]. They propose a dynamic altitude adjustment scheme that optimizes the coverage and Ref.…”

A new connectivity model for unmanned aerial vehicle communications and flying height optimization

“…While this provides a mathematical form that is amenable to analysis, it can be far from an accurate model, since, in practice, the link will either be LoS or non-LoS (NLoS), not an average of the two. This approach of averaging the path-loss has been taken almost universally, including in References 4,5,15,[17][18][19][20][21][22]24,[26][27][28][29][30][31] Two exceptions are References 33 and 34 however they suffer from the fact that they used the inaccurate model for probability of LoS from Reference 9. In this paper, we highlight the inaccuracy of averaging over the LoS and non-LoS cases, and present a new approach to height optimization that almost exactly matches with Monte-Carlo simulation.…”

“…Many papers have used the probability of LoS models from References 9‐14 when addressing specific problems of UAV communications, including 4,15‐34 . As such, they all suffer from the inaccuracy of those models, from either making 1D geographical approximations or assuming totally randomised building location models.…”

“…While this provides a mathematical form that is amenable to analysis, it can be far from an accurate model, since, in practice, the link will either be LoS or non‐LoS (NLoS), not an average of the two. This approach of averaging the path‐loss has been taken almost universally, including in References 4,5,15,17‐22,24,26‐31. Two exceptions are References 33 and 34 however they suffer from the fact that they used the inaccurate model for probability of LoS from Reference 9.…”

“…Many papers have used the probability of LoS models from References 9-14 when addressing specific problems of UAV communications, including. 4,[15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34] As such, they all suffer from the inaccuracy of those models, from either making 1D geographical approximations or assuming totally randomised building location models. In Reference 35, we presented a significantly more accurate probability of LoS model that can be applied in all of those applications.…”