Performance Enhancement of UAV-Based Cognitive Radio Network

Bala, Indu; Mandal, Danvir; Singhal, Ankur

doi:10.1007/978-981-16-8721-1_10

Cited by 4 publications

(8 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In view of the sequence {M k } of ( 19), since ℐ k−1 ⊂ ℐ k , the iterative property of conditional expectation E{E{P(X|𝒢 )|ℋ }} = E{X|ℋ } (ℋ is a sub − 𝜎 − field of 𝒢 ) implies (20). Similar to…”

Section: Bayesian Detection Methods With Feedbackmentioning

confidence: 98%

See 1 more Smart Citation

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

Section: Bayesian Detection Methods With Feedbackmentioning

confidence: 98%

“…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%

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

“…In view of a comprehensive survey on the application of hypothesis test in the field of spectrum sensing in cognitive UAV or radio networks, aforementioned works are divided into three categories according to the type and purpose of the hypothetical test used and the network scenario, as shown in Table 1. Unlike previous works, more efforts on CSS of cognitive UAV networks have been made by three aspects: (1) We integrate the spectrum sensing problem into cognitive UAV network and achieve cooperative gain from multiple slots within a single sensing slot; (2) We propose Bayesian quick detection to realize CSS among multiple slots and derive a tradeoff between the sensing performance and efficiency; (3) We covert the tradeoff optimization into a stopping time problem and derive the closed-form expression of the optimal stopping time.…”

Section: Related Work and Motivationmentioning

confidence: 99%

“…The traditional fixed spectrum management makes spectrum resources suffer from a serious shortage 1 . When faced with the deployment of the UAV communication system, the situation of the originally scarce spectrum resources is further aggravated 2 . However, Federal Communications Commission (FCC) research proves that the utilization rate of the frequency spectrum is between 15% and 85% in time and space.…”

Section: Introductionmentioning

confidence: 99%

“…1 When faced with the deployment of the UAV communication system, the situation of the originally scarce spectrum resources is further aggravated. 2 However, Federal Communications Commission (FCC) research proves that the utilization rate of the frequency spectrum is between 15% and 85% in time and space. That is to say, spectrum resources are idle to varying degrees in time and space, resulting in low spectrum utilization.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Bayesian quickest detection‐based spectrum sensing for cognitive UAV networks

Liang

2023

Trans Emerging Tel Tech

View full text Add to dashboard Cite

With the gradual popularization of unmanned aerial vehicles (UAVs), spectrum resources are increasingly scarce. At this time, cognitive radio (CR) technology came into being, which can provide available spectrum resources for UAVs. However, different from the conventional CR networks, the UAV's location flexibility significantly reduces the spectrum sensing performance and increases the detection delay. Therefore, we propose a Bayesian quickest detection-based spectrum sensing for cognitive UAV networks in this paper. To this aim, we establish a multislot spectrum sensing model in a cognitive UAV network to realize cooperative spectrum sensing (CSS) within the single UAV and avoid disadvantages of cooperation among multiple UAVs. On the basis of this, we make use of Bayesian quickest detection to implement multislot spectrum sensing and evaluate its false alarm probability and detection delay. Further, the tradeoff problem between the false alarm and the detection delay is developed and converted into an optimization stopping time problem. Subsequently, the stopping time problem is theoretically proved and solved. Finally, compared to Bayesian detection, numerical simulation results show that the superiority of Bayesian quickest detection with respect to the false alarm probability and the detection delay is evident, which proves the correctness and effectiveness of the proposed tradeoff problem.

show abstract

UAV aided virtual cooperative spectrum sensing for cognitive radio networks

Gul,

Kim,

Ali

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

Cooperative spectrum sensing (CSS) involves multiple secondary users (SUs) reporting primary user (PU) channel sensing states to the fusion center (FC). However, the high overheads associated with multi-user CSS impose power limitations that limit its usefulness in unmanned aerial vehicle (UAV) networks. To address this challenge, we propose a virtual CSS, where a single UAV conducts CSS while following a circular flight trajectory in the air. The novelty of our approach is presenting a working frame structure for the UAV flight, including sensing and data transmission periods with further division of the sensing time into mini-sensing slots. In the virtual CSS, UAV performs local sensing decisions in each mini-slot and accumulates them for a final decision. The proposed virtual CSS scheme exploits sequential decision fusion (SDF), which sequentially adds individual mini-slot decisions. Additionally, we leverage machine learning (ML), employing AdaBoost ensembling classifier (ENC), to inspect flight conditions and reconfigure mini-slot periods dynamically for both traditional decision fusion (TDF) and our proposed SDF schemes. Furthermore, we identify an optimal decision threshold (ODT) for the proposed SDF, enabling the comparison of sequential results with an adjustable threshold through majority voting. This novel approach results in energy efficiency and improved throughput for virtual CSS using SDF, surpassing the performance of TDF, which relies on collecting entire mini-slot reports for its final decision. Simulation results demonstrate the effectiveness of the proposed SDF following the ENCODT (SDF-ENCODT) scheme compared to existing techniques from the literature. We explore varying levels of UAV flight velocities, moving radius, detection probability demand, and channel signal-to-noise ratio (SNR), reinforcing the significance of our contribution. Our research highlights the motivation to address spectrum scarcity in UAV communication by proposing an innovative virtual CSS scheme based on SDF. The proposed approach enhances spectrum utilization, overcomes power limitations, and substantially improves CSS for UAV networks.

show abstract

Performance Enhancement of UAV-Based Cognitive Radio Network

Cited by 4 publications

References 17 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

Bayesian quickest detection‐based spectrum sensing for cognitive UAV networks

UAV aided virtual cooperative spectrum sensing for cognitive radio networks

Contact Info

Product

Resources

About