Modeling and Performance Analysis in UAV Assisted Ultra Dense Networks

Qi, Lin; Yan, Shi; Peng, Mugen

doi:10.1109/iccw.2018.8403518

“…To overcome the unusual traffic challenge of unexpected events, a resilient UDN using UAVs as flying BSs is designed to serve an acceptable QoS [78]. In [79], the authors introduced two sub-channel access schemes in the UAV-aided UDNs to decrease the interference from adjacent UAVs. In…”

Section: H Unmanned Aerial Vehicles (Uavs)mentioning

confidence: 99%

Practical Optimization and Game Theory for 6G Ultra-Dense Networks: Overview and Research Challenges

Tinh

¹

,

Nguyen

²

,

Kha

³

et al. 2022

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Ultra-dense networks (UDNs) have been employed to solve the pressing problems in relation to the increasing demand for higher coverage and capacity of the fifth generation (5G) wireless networks. The deployment of UDNs in a very large scale has been envisioned to break the fundamental deadlocks of beyond 5G or the sixth generation (6G) networks and deliver many more orders of magnitude gains that today's technologies achieve. However, the mathematical tool to optimize the system performance under the stringent radio resource constraints is widely recognized to be a formidable challenge. System-level performance optimization of current UDNs are usually conducted by relying on numerical simulations, which are often time-consuming and have become extremely difficult in the context of 6G with extremely high density. As such, there is an urgent need for developing a realistic mathematical model for optimizing the 6G UDNs. In this paper, we introduce challenges as well as issues that have to be thoroughly considered while deploying UDNs in realistic environment. We revisit efficient mathematical techniques including game theory and real-time optimization in the context of optimizing UDNs performance. In addition, emerging technologies which are suitable to apply in UDNs are also discussed. Some of them have already been used in UDNs with high efficiency while the others which are still under investigation are expected to boost the performance of UDNs to achieve the requirements of 6G. Importantly, for the first time, we introduce the joint optimal approach between realtime optimization and game theory (ROG) which is an effective tool to solve the optimization problems of large-scale UDNs with low complexity. Then, we describe two approaches for using ROG in UDNs. Finally, some case study of ROG are given to illustrate how to apply ROG for solving the problems of different applications in UDNs.

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“…The foreseen potential of UAVs' to support massive wireless connectivity has attracted significant research efforts to model, analyze, and design UAVs networks. Instances of such research efforts include developing air-to-air and air-to-ground communications channel models [7], [8], optimal placement and resource allocation for UAVs [9], [10], trajectory planning [4], [11], [12], charging ground sensors using UAVs radio frequency (RF) signals [13], [14] and evaluating the performance of UAV-assisted cellular networks [15]- [17].…”

Section: A Related Workmentioning

confidence: 99%

“…The detailed proof is omitted here and S n (τ b ) is given in (18). Finally, by the law of total probability, the backhaul probability can be derived as in (17).…”

Section: F Proof Of Theoremmentioning

confidence: 99%

Analysis of Large Scale Aerial Terrestrial Networks with mmWave Backhauling

Kouzayha

¹

,

Sawy

²

,

Dahrouj

³

et al. 2021

IEEE Trans. Wireless Commun.

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Service providers are considering the use of unmanned aerial vehicles (UAVs) to enhance wireless connectivity of cellular networks. To provide connectivity, UAVs have to be backhauled through terrestrial base stations (BSs) to the core network. In particular, we consider millimeter-wave (mmWave) backhauling in the downlink of a hybrid aerial-terrestrial network, where the backhaul links are subject to beamforming misalignment errors. In the proposed model, the user equipment (UE) can connect to either a ground BS or a UAV, where we differentiate between two transmission schemes according to the backhaul status. In one scheme, the UEs are served by the UAVs regardless of whether the backhaul links are good or not. In the other scheme, the UAVs are aware of the backhaul links status, and hence, only the subset of successfully backhauled UAVs can serve the UEs. Using stochastic geometry, the performance of the proposed model is assessed in terms of coverage probability and validated against Monte-Carlo simulations. Several insights are provided for determining some system parameters including the UAVs altitude and required number and the beamforming misalignment error of the backhaul link. The obtained results highlight the impact of the UAVs backhaul link on the UE experience.

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“…in Lemma 1 and P[SINR > τ b ] = S(τ b ) is the backhaul probability given in (17). The service failure probability is defined asÃ f = P[s = u l , SINR < τ b s = u n , SINR < τ b ] and derived as…”

Section: A Transmission Probabilitiesmentioning

confidence: 99%

“…where s 2 l = m l τay η l u l Pu and S(τ b ) is the backhaul probability given in (17), (a) follows from the independence assumption, (b) from the CCDF of the UE-UAV channel power gain Ω u l ,0 and from the expression of the interference I agg,u l =Î u l + I g l . Finally, (c) follows from the independence ofÎ u l and I g l , whereÎ u l and I g l are the interference expressions from all the UAVs except the serving UAV and from all the BSs.…”

mentioning

confidence: 99%

Stochastic Geometry Analysis of Hybrid Aerial Terrestrial Networks with mmWave Backhauling

Kouzayha

¹

,

ElSawy

²

,

Dahrouj

³

et al. 2020

ICC 2020 - 2020 IEEE International Conference on Communications (ICC)

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Service providers are considering the use of unmanned aerial vehicles (UAVs) to enhance wireless connectivity of cellular networks. To provide connectivity, UAVs have to be backhauled through terrestrial base stations (BSs) to the core network. In particular, we consider millimeter-wave (mmWave) backhauling in the downlink of a hybrid aerial-terrestrial network, where the backhaul links are subject to beamforming misalignment errors. In the proposed model, the user equipment (UE) can connect to either a ground BS or a UAV, where we differentiate between two transmission schemes according to the backhaul status. In one scheme, the UEs are served by the UAVs regardless of whether the backhaul links are good or not. In the other scheme, the UAVs are aware of the backhaul links status, and hence, only the subset of successfully backhauled UAVs can serve the UEs. Using stochastic geometry, the performance of the proposed model is assessed in terms of coverage probability and validated against Monte-Carlo simulations. Several insights are provided for determining some system parameters including the UAVs altitude and required number and the beamforming misalignment error of the backhaul link. The obtained results highlight the impact of the UAVs backhaul link on the UE experience.

show abstract

Modeling and Performance Analysis in UAV Assisted Ultra Dense Networks

Cited by 12 publications

References 13 publications

Practical Optimization and Game Theory for 6G Ultra-Dense Networks: Overview and Research Challenges

Practical Optimization and Game Theory for 6G Ultra-Dense Networks: Overview and Research Challenges

Analysis of Large Scale Aerial Terrestrial Networks with mmWave Backhauling

Stochastic Geometry Analysis of Hybrid Aerial Terrestrial Networks with mmWave Backhauling

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