Energy-Efficiency Joint Trajectory and Resource Allocation Optimization in Cognitive UAV Systems

Liang, Xiaopeng; Deng, Qian; Shu, Feng; Wang, Jiangzhou

doi:10.1109/jiot.2022.3185674

Cited by 9 publications

(3 citation statements)

References 35 publications

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“…In the past, the multi‐UAV CSS framework for efficiently achieving spectrum resources has been extensively studied in cognitive UAV networks, such as References 10–30. In detail, a comprehensive survey is given in Table 1.…”

Section: Introductionmentioning

confidence: 99%

“…Based on the consideration of flight trajectory and Reference 19, the throughput optimization problem is studied in References 23, 24, and the sensing duration, threshold, transmission power and following UAV trajectory in cognitive UAV networks are jointly optimized. In Reference 25, Y. Gu et al optimized the sensing radian and flight trajectory of UAV from a resource allocation perspective.…”

Section: Introductionmentioning

confidence: 99%

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Bayesian detection with feedback for cooperative spectrum sensing in cognitive UAV networks

Wu,

Su,

Qiao

et al. 2024

Trans Emerging Tel Tech

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Unmanned aerial vehicles (UAVs) are becoming a popular research topic in applications that do not require human intervention. A variety of applications and devices coexist in the environment where UAVs operate, resulting in a serious spectrum shortage. Therefore, cognitive radio (CR) is a promising solution for opportunistic access to underutilized spectrum bands by the primary user (PU) through cooperative spectrum sensing (CSS) technique. However, the flexible location of UAVs makes CSS inefficient and even difficult to be implemented. In view of this, a cognitive UAV network model consisting of a pair of UAVs which follows a circular flight trajectory to participate in CSS is proposed in a spectrum sensing frame structure. According to the local energy detection, we further propose an optimization problem about the stopping time in a quickest detection paradigm and conduct out Bayesian detection method with feedback to minimize the sensing delay and the false alarm probability by optimizing the stopping time. Moreover, we theoretically derive the optimal threshold pair and prove the optimal stopping time by means of Markov process. At last, a series of numerical simulations are shown to corroborate the proposed Bayesian detection method with feedback, in terms of the false alarm probability, the sensing delay, and achievable throughput. In contrast to the classic Neyman‐Pearson and Bayesian detection methods, the advantage of Bayesian detection method with feedback sensing is presented.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bayesian detection with feedback for cooperative spectrum sensing in cognitive UAV networks

Wu,

Su,

Qiao

et al. 2024

Trans Emerging Tel Tech

View full text Add to dashboard Cite

show abstract

“…The optimization of the UAV energy in terms of transmission and propulsion levels has been investigated in [11], [12]. In [13], the authors explored a two-UAV system where a moving following UAV is transmitting data to a leading UAV in the CR interweave context where the spectrum sensing process is optimized. Finally, UAVs have been employed as cooperative flying relays enabling coverage expansion for primary and secondary devices.…”

Section: Introductionmentioning

confidence: 99%

Integrating Cognitive Radio MIMO UAVs in Cellular Networks for 5G and Beyond

Sboui¹,

Ghazzai

Massoud

2022

2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME)

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In this paper, we study the performance of deploying multiple unlicensed unmanned aerial vehicles (UAVs) to share the spectrum and the base station of an established primary network in Multiple-Input-Multiple-Output (MIMO) communications. We present a novel power allocation scheme based on a combination of underlay cognitive radio with channel precoding and decoding to maximize the UAVs sum-rate. In the proposed scheme, we jointly use space alignment and successive interference cancellation techniques to minimize and avoid the interference caused on the primary communication. Optimal power allocation scheme for the proposed system is devised. In our simulations, we show that the proposed scheme allows the UAVs to achieve a rate that highly depends on the primary power and interference threshold. In addition, a supplementary rate is achieved when the UAVs transmit in the unused parallel channels called free eigenmodes. The impact of the different system parameters including the number of antennas and the 3D position of the UAVs are also evaluated.

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IRS-Assisted Cognitive UAV Networks: Joint Sensing Duration, Passive Beamforming and Three-Dimensional Location Optimization

Deng

Liang

et al. 2023

IEEE Internet Things J.

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Energy-Efficiency Joint Trajectory and Resource Allocation Optimization in Cognitive UAV Systems

Cited by 9 publications

References 35 publications

Bayesian detection with feedback for cooperative spectrum sensing in cognitive UAV networks

Bayesian detection with feedback for cooperative spectrum sensing in cognitive UAV networks

Integrating Cognitive Radio MIMO UAVs in Cellular Networks for 5G and Beyond

IRS-Assisted Cognitive UAV Networks: Joint Sensing Duration, Passive Beamforming and Three-Dimensional Location Optimization

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