AI-Based Abnormality Detection at the PHY-Layer of Cognitive Radio by Learning Generative Models

Toma, Andrea; Krayani, Ali; Farrukh, Muhammad; Qi, Haoran; Gao, Yue; Regazzoni, Carlo S.

doi:10.1109/tccn.2020.2970693

Cited by 40 publications

(40 citation statements)

References 30 publications

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“…respectively. The numerical results are presented in Section V to verify the validity of (26) and (27) at the medium and high power cases.…”

Section: ) Linear Eh Casementioning

confidence: 99%

“…Content may change prior to final publication. Substituting (27) and (28) into (17), the approximate AER under the linear EH model is given by…”

Section: ) Linear Eh Casementioning

confidence: 99%

“…To overcome this problem, the artificial neural network based on deep learning is introduced to learn the best transmit power or position for the relay under the linear and nonlinear EH models. The well-trained DFNN has proven to be effective in approximating any measurable bounded function to the expected precision by performing complex feature transform [27]- [30], [42], and thus is a proper method to solve (P0), (P1), and (P2).…”

Section: Learning-based Optimizationmentioning

confidence: 99%

“…Similar to [27], [28], the DFNN is employed to realize the nonlinear mapping from any given pair (x R , y R ) m to an optimal transmit SNR γ * R or from any one given γ R to its best position (x * R , y * R ) m that maximize AER. Without loss of generality, the adopted DFNNs are composed of three parts in the scheme, including the input, hidden and output layers.…”

Section: A Separate Optimization Of Power and Locationmentioning

confidence: 99%

“…On the other hand, machine learning has proven to be an efficient method to settle the complex problems with security constraints in wireless networks [26], [27]. In a nutshell, the hot-booting Q-learning based power allocation scheme was put forward in [28] to resist an intelligent jammer without knowing the jamming and channel models.…”

Section: Introductionmentioning

confidence: 99%

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Power and Location Optimization of Full-Duplex Relay for Proactive Eavesdropping Networks

et al. 2020

IEEE Access

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This work investigates the proactive eavesdropping through a friendly full-duplex (FD) relay and a legitimate monitor over the wireless-powered suspicious communication network, where one suspect source performs energy harvesting (EH) from one dedicated power beacon and then communicates with one suspect destination. Under this setup, a relay-aided proactive eavesdropping scheme is designed to improve the surveillance performance of the system. The closed-form expressions are derived under linear and nonlinear EH models, including the decoding outage probability for the suspicious link, the eavesdropping outage probability for the eavesdropping link, and the average eavesdropping rate (AER). Based on these obtained expressions, an optimization problem to maximize the AER is formulated by jointly optimizing the power and location of the relay. The existence of the optimal solution is carefully analyzed. Moreover, the separate optimization issues of the power and location are first established, and then a bisection-based searching algorithm is presented for the joint optimization problem with the linear EH model. Furthermore, to reduce the complexity of joint optimization, a low-complexity learning-based iteration algorithm is further proposed by employing the well-fitting characteristic of the deep feedforward neural network. Numerical results verify the effectiveness of the proposed algorithm, and show that the optimized FD relay-aided proactive eavesdropping scheme outperforms the reference schemes. Finally, the application scenarios of the proposed proactive eavesdropping scheme are discussed. INDEX TERMS Proactive eavesdropping, wireless power transfer, average eavesdropping rate, deep feedforward neural network, power optimization.

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“…respectively. The numerical results are presented in Section V to verify the validity of (26) and (27) at the medium and high power cases.…”

Section: ) Linear Eh Casementioning

confidence: 99%

“…Content may change prior to final publication. Substituting (27) and (28) into (17), the approximate AER under the linear EH model is given by…”

Section: ) Linear Eh Casementioning

confidence: 99%

Section: Learning-based Optimizationmentioning

confidence: 99%

Section: A Separate Optimization Of Power and Locationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Power and Location Optimization of Full-Duplex Relay for Proactive Eavesdropping Networks

et al. 2020

IEEE Access

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To mitigate primary user emulation attack trajectory using cognitive single carrier frequency division multiple access approaches: Towards next generation green IoT

Mazumdar

Gohel

2023

Engineering Reports

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The growing cognizance of spectrum scarcity has become a more significant concern in wireless radio communications. Due to the exponential growth of data transmission in intelligent wireless sensor networks, energy spectrum detection has become a promising solution for resolving spectrum shortages. Primary user emulation attack (PUEA) has been identified as a significant attack vector in the cognitive radio (CR) domain's physical layer. In comparison, the CR is a promising method to increase spectrum efficiency by allowing unlicensed secondary users (SUs) to access licensed frequency bands without interfering with approved primary users (PUs). The study's primary findings are the methodology for preventing PUEA using authentication tags, which are unique sequences. This research blends SC‐FDMA with CR to protect CR networks from PUEA attacks, a Latin square (LS) matrix tag generation system is proposed to mitigate the PUEA effect. The technology is meant to provide effective authentication and protection against malicious users. In a secured environment, the LS tag technique is utilized to track and estimate the PU. For example, the BER of both techniques is virtually identical between 0 and 4 dB, while the BER performance of the suggested LS tag generation improves with increasing signal‐to‐noise ratio (SNR). As a result, the suggested LS tag generation is less susceptible to PUEA. To diminish the influence of PUEA in CR networks, an efficient enlightening approach for making the future Green Cognitive Radio Wireless networks structure is proposed. The simulation results also demonstrate the resilience of the proposed CR spectrum sensing techniques for energy‐efficient knowledge at varying degrees to reduce the adverse effects of environmental technologies.

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On Platform to Enable the Cognitive Radio Over 5G Networks

Hindia

Qamar

Ojukwu

et al. 2020

Wireless Pers Commun

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With the increase in the number of communication devices, the requirement for higher bandwidth is essential. To achieve this goal, research and industrial communities have both suggested that future wireless systems will take advantage of the numerous emerging technologies. Utilization of Cognitive Radio (CR) for the next-generation Fifth Generation (5G) communication technology is the major advancement for getting a higher bandwidth in a cellular communication network. In this paper, we present a comprehensive study of CR from the perspectives of spectrum allocation schemes, impact and role of MAC layer in spectrum sensing and sharing, CR application in multi-hop wireless networks, and challenges associated with channel selection and packet routing in multi-hop heterogeneous CR networks. This paper also presents the analysis, in literature, of a range of intelligent routing protocols that are considered viable for packets routing in CR networks. The need to address the issue of spectrum depletion and the apparent underutilization of available scarce spectrum resources in existing wireless networks is the primary motivation behind this study. Considering the fact that CR technology can potentially maximize the utilization of bulk of the unused communication spectrum bands for the future 5G of wireless network and beyond.

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AI-Based Abnormality Detection at the PHY-Layer of Cognitive Radio by Learning Generative Models

Cited by 40 publications

References 30 publications

Power and Location Optimization of Full-Duplex Relay for Proactive Eavesdropping Networks

Power and Location Optimization of Full-Duplex Relay for Proactive Eavesdropping Networks

To mitigate primary user emulation attack trajectory using cognitive single carrier frequency division multiple access approaches: Towards next generation green IoT

On Platform to Enable the Cognitive Radio Over 5G Networks

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