Optimization of Spectrum Efficiency in UAV Cognitive Communication Network Based on Trajectory Planning

Gu, Yilong; Huang, Yangchao; Yuetong, Zhang; An, Qi; Han, Huizhu; Fu, Ying; ZHANG, YANHUI

doi:10.1145/3507509.3507514

Cited by 3 publications

(3 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [16], the virtual CSS optimization problem was taken into consideration in a UAV-based interweave CR system. The optimization problem of sensing radian and the real-time trajectory of UAV was proposed in [17] to improve SE. In [18], X. Liu et al proposed virtual CSS to maximize effective throughput by jointly optimizing the local sensing arc and the number of sensing time slots.…”

Section: Related Work and Motivationmentioning

confidence: 99%

SVM Dynamic Selection of Voting Rule for Cooperative Spectrum Sensing in CUAVNs

Yu,

Shen,

et al. 2024

Preprint

View full text Add to dashboard Cite

Due to the rapid development of unmanned aerial vehicles (UAVs) communication technology, UAVs are gradually competing with primary users (PUs) for spectrum resources. Cognitive radio (CR) is a promising solution to meet the needs. Cooperative spectrum sensing (CSS) is considered as an effective method to detect the PU signal and identify available spectrum resources for UAVs in a cognitive UAV network (CUAVN). However, the cooperative mode among multiple UAVs may incur a high overhead, resulting in performance degradation. Therefore, we introduce a differential sequential 1 (DS1), which incorporates a differential mechanism and leverages the sequential idea based on the classical voting rule to enhance cooperative performance and efficiency. In view of this, we formulate three scenarios to characterize the PU activity and introduce a multi-slot cooperative mode within a single UAV to realize cooperative gain. Further, only the information change about the PU status is sequentially calculated in DS1, and combined with a sequential idea, the efficiency of the voting rule is greatly improved. Moreover, the application of support vector machines (SVM) in dynamic selection enables the selection of the most suitable voting rule based on diverse sensing parameters. This dynamic selection process ensures optimal performance and efficiency by adapting the voting rule to the specific characteristics of the given scenario. Finally, simulation results demonstrate that the superiority of our proposal with respect to the detection performance, sample size and the energy efficiency is evident, which proves the high performance of the proposed policy.

show abstract

Section: Related Work and Motivationmentioning

confidence: 99%

SVM Dynamic Selection of Voting Rule for Cooperative Spectrum Sensing in CUAVNs

Yu,

Shen,

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…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. Although these studies incorporate flight trajectory/planning into optimization problem, they still do not essentially solve the problem that multi‐UAV CSS is difficult to achieve due to the sensing environment.…”

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

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.

show abstract