Throughput Optimization for Cognitive UAV Networks: A Three-Dimensional-Location-Aware Approach

Liang, Xiaopeng; Xu, Wenjun; Gao, Hui; Pan, Miao; Lin, Jiaru; Deng, Qian; Zhang, Ping

doi:10.1109/lwc.2020.2974946

Cited by 31 publications

(23 citation statements)

References 10 publications

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“…For the CUAVN, it is assumed that the line-of-sight (LoS) of air-to-air channels dominates between the FUAVs and the LUAV, and we model this with a free-space path loss formulation [20]. Furthermore, the receiver is assumed to be able to compensate for the Doppler frequency shift caused by UAV mobility [16].…”

Section: A System Modelmentioning

confidence: 99%

“…Among the three sub-problems decomposed from the original non-convex problem (20), the subproblems OP 1 and OP 2 are convex problems, and the subproblem OP 3 can be approximately converted into a convex problem by introducing auxiliary variables and DC functions. Hence, it is feasible to solve each subproblem with certain fixed variables.…”

Section: Convergence and Complexity Analysismentioning

confidence: 99%

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Joint Trajectory Optimization and Spectrum Access for Cognitive UAV Networks

et al. 2020

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In this paper, we consider a cognitive unmanned aerial vehicle network (CUAVN), which consists of a leading unmanned aerial vehicle (LUAV) and a group of following unmanned aerial vehicles (FUAVs), and where the FUAVs fly irregularly from a data collection point to the next data collection point and transmit collected data to the LUAV. Our goal is to maximize the achievable throughput of the CUAVN by jointly adapting the FUAVs sensing duration, the trajectory of the FUAVs, and the transmit power of the FUAVs under the constraints of maximum interference, maximum UAV speed, UAV collision avoidance and the total flying periods. In the challenging scenarios, the original non-convex problem is addressed by iteratively optimizing three decoupled subproblems: sensing duration optimization, FUAVs trajectory optimization, and FUAVs power allocation optimization. Since there are highly non-convex objective function and constraints in the FUAV trajectory optimization subproblem, we convert it into a more tractable convex problem by utilizing difference-of-convex (DC) functions. Furthermore, a three dimensional (3D) spatial-temporal sensing scheme based on UAVs is introduced into cognitive UAV systems with multiple primary transmit-receive pairs (PTRs) to further improve spectrum efficiency. Simulation results show that the achievable throughput of the FUAVs with the proposed scheme rises by approximately 53% compared with the FUAV fixed trajectory flight scheme and by approximately 58% in comparison with the only timedimension spectrum sensing scheme.

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Section: A System Modelmentioning

confidence: 99%

Section: Convergence and Complexity Analysismentioning

confidence: 99%

Joint Trajectory Optimization and Spectrum Access for Cognitive UAV Networks

et al. 2020

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“…In [9], considering the downlink transmission of UAV-enabled networks in coexistence with D2D communication, the authors proposed a joint design of D2D assignment and resource allocation for maximising ground terminals throughput. In addition, in [10], the authors have formulated and solved the throughput maximisation problem, by jointly combining optimal location and spectrum sensing duration of the UAVs. However, the cognitive UAV network considered in [10] only consists of a single (primary) receiver, which is generally different from our model.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, in [10], the authors have formulated and solved the throughput maximisation problem, by jointly combining optimal location and spectrum sensing duration of the UAVs. However, the cognitive UAV network considered in [10] only consists of a single (primary) receiver, which is generally different from our model. Moreover, aiming at maximising a SU's throughput, the work in [11] studied the joint optimisation problem of the UAV placement and power allocation.…”

Section: Introductionmentioning

confidence: 99%

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Spectrum-Sharing UAV-Assisted Mission-Critical Communication: Learning-Aided Real-Time Optimisation

et al. 2021

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Resource optimization in UAV‐assisted wireless networks—A comprehensive survey

Basharat

Naeem

Qadir

et al. 2022

Trans Emerging Tel Tech

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Unmanned aerial vehicles (UAVs) are inevitable to meet the requirements of future wireless networks. Recently, researchers have investigated diverse issues related to UAV‐assisted networks (including placement of UAVs, resource management, and spectrum sharing) for a broad range of applications, including disaster management, data collection from the ground sensor network, surveillance, logistic support, etc. This article presents a comprehensive survey of recent advances in UAV‐assisted networks. We mainly emphasize the optimization perspective of UAV‐assisted wireless networks with different objectives, including coverage area, throughput, energy efficiency, quality of service, delay, and outage probability. We provide a detailed discussion for each objective with their constraints, optimization problem, solution approach, and performance metrics. We also provide relationships among different objectives and parameters considered in the literature. Finally, we list open research issues and future research directions to improve UAV‐assisted wireless networks in the context of optimization.

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Throughput Optimization for Cognitive UAV Networks: A Three-Dimensional-Location-Aware Approach

Cited by 31 publications

References 10 publications

Joint Trajectory Optimization and Spectrum Access for Cognitive UAV Networks

Joint Trajectory Optimization and Spectrum Access for Cognitive UAV Networks

Spectrum-Sharing UAV-Assisted Mission-Critical Communication: Learning-Aided Real-Time Optimisation

Resource optimization in UAV‐assisted wireless networks—A comprehensive survey

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