Interference Avoidance in UAV-Assisted Networks: Joint 3D Trajectory Design and Power Allocation

Rahmati, Ali; Hosseinalipour, Seyyedali; Yapıcı, Yavuz; He, Xiaofan; Güvenç, İsmail; Dai, Huaiyu; Bhuyan, Arupjyoti

doi:10.1109/globecom38437.2019.9013532

Cited by 20 publications

(7 citation statements)

References 23 publications

(41 reference statements)

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“…The goal of OUT-SIA is to minimize the TTT for all FBS's trajectories. The constraint in (26) ensures that if the variable y ij is 1, then the y ji is 0 and vice versa. The constraint in (27) ensures that if the variable y ij is 1, then the service at CP i without overlap of the service at CP j under the UDG is guaranteed.…”

Section: B Interference-aware Uav Trajectory Planning Formulation Wit...mentioning

confidence: 99%

Interference-aware path planning optimization for multiple UAVs in beyond 5G networks

Lee

Friderikos

2022

J. Commun. Netw.

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Section: B Interference-aware Uav Trajectory Planning Formulation Wit...mentioning

confidence: 99%

Interference-aware path planning optimization for multiple UAVs in beyond 5G networks

Lee

Friderikos

2022

J. Commun. Netw.

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“…Firstly, it is worth to investigate the joint resource allocation and trajectory design for multi-IRS-assisted UAV communications. Besides, introducing multiple UAVs to the considered system with concurrent transmission has the potential to further improve the system performance [41], [42]. On the other hand, a practical design to address the vulnerability to the potential jamming/eavesdropping attacks of IRS-assisted UAV communication systems is also interesting and desired.…”

Section: A Sum-rate Maximization Problem Formulationmentioning

confidence: 99%

Sum-Rate Maximization for IRS-Assisted UAV OFDMA Communication Systems

Wei

Cai

Sun

et al. 2021

IEEE Trans. Wireless Commun.

170

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In this paper, we consider the application of intelligent reflecting surface (IRS) in unmanned aerial vehicle (UAV)-based orthogonal frequency division multiple access (OFDMA) communication systems, which exploits both the significant beamforming gain brought by the IRS and the high mobility of UAV for improving the system sum-rate. The joint design of UAV's trajectory, IRS scheduling, and communication resource allocation for the proposed system is formulated as a non-convex optimization problem to maximize the system sumrate while taking into account the heterogeneous quality-ofservice (QoS) requirement of each user. The existence of an IRS introduces both frequency-selectivity and spatial-selectivity in the fading of the composite channel from the UAV to ground users. To facilitate the design, we first derive the expression of the composite channels and propose a parametric approximation approach to establish an upper and a lower bound for the formulated problem. An alternating optimization algorithm is devised to handle the lower bound optimization problem and its performance is compared with the benchmark performance achieved by solving the upper bound problem. Simulation results unveil the small gap between the developed bounds and the promising sum-rate gain achieved by the deployment of an IRS in UAV-based communication systems.

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“…A joint trajectory and power control-based interference management is proposed in [30], where relaying via multiple FlyBSs is assumed and neighboring macro base stations, small cells, and jammers are considered as a sources of interference. The trajectories of the FlyBSs are optimized by the spectral graph theory and, then, the transmission power of the FlyBSs is optimized.…”

Section: Related Workmentioning

confidence: 99%

Soft Frequency Reuse With Allocation of Resource Plans Based on Machine Learning in the Networks With Flying Base Stations

Hossain

Bečvář

2021

IEEE Access

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Flying base stations (FlyBSs) enable ubiquitous communications in the next generation mobile networks with a flexible topology. However, a deployment of the FlyBSs intensifies interference, which can result in a degradation in the throughput of cell-edge users. In this paper, we introduce a flexible soft frequency reuse (F-SFR) that enables a self-organization of a common SFR in the networks with an unpredictable and dynamic topology with the FlyBSs. We propose a graph theory-based algorithm for an allocation of resource plans, which is understood as a bandwidth allocation and a transmission power setting in the context of SFR. Furthermore, we introduce a low-complexity implementation of the proposed resource allocation using deep neural network (DNN) to significantly reduce the computation complexity. We show that the proposed F-SFR increases the throughput of cell-edge users by 16% to 26% and, at the same time, improves the satisfaction of the cell-edge users by up to 25% compared to the state-of-the-art solutions. We also demonstrate that the proposed scheme ensures a higher fairness in the throughput among the users with respect to the state-of-the-art solutions. The implementation via DNN also outperforms all state-of-the-art solutions despite its very low complexity.

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Interference Avoidance in UAV-Assisted Networks: Joint 3D Trajectory Design and Power Allocation

Cited by 20 publications

References 23 publications

Interference-aware path planning optimization for multiple UAVs in beyond 5G networks

Interference-aware path planning optimization for multiple UAVs in beyond 5G networks

Sum-Rate Maximization for IRS-Assisted UAV OFDMA Communication Systems

Soft Frequency Reuse With Allocation of Resource Plans Based on Machine Learning in the Networks With Flying Base Stations

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