Energy efficiency of full-duplex cognitive radio in low-power regimes under imperfect spectrum sensing

Ranjbar, Mohammad; Nguyen, H. L.; Tran, Nghi H.; Karacolak, Tutku; Sastry, Shivakumar; Nguyen, Long D.

doi:10.1007/s11036-021-01755-z

Cited by 5 publications

(2 citation statements)

References 52 publications

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“…The CR has effectively solved the issue of spectrum scarcity in the wireless industry by enabling SUs to access the unused band of PUs [2]. Spectrum sensing is the primary operation of the CR, where a SU searches for available spectrum [3].…”

Section: Introductionmentioning

confidence: 99%

Detection of Emulation Attacks in Cognitive Radio Networks using Heuristic Techniques

Mahmood,

Qamar,

Latif

2024

NIJEC

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This paper discusses the issue of primary user emulation attacks (PUEAs) in cognitive radio networks (CRNs). These attackers imitate the signal characteristics of primary users (PUs), preventing secondary users (SUs) from accessing their assigned spectrum. The research focuses on detecting PUEAs using the time difference of arrivals (TDOA) to detect the attacker and reduce detection errors using heuristic techniques. Differential evolution (DE) and cuckoo search (CS) algorithms are utilized to optimize detection accuracy. Simulation results confirm that the proposed CS algorithm outperforms and gives better performance as compared to the DE algorithm.

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Section: Introductionmentioning

confidence: 99%

Detection of Emulation Attacks in Cognitive Radio Networks using Heuristic Techniques

Mahmood,

Qamar,

Latif

2024

NIJEC

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“…Usually, the performance of SS is evaluated by false alarm probability and detection probability [6]. A lower false alarm probability denotes a higher system throughput while a higher detection probability signifies a better capability to protect PUs.…”

Section: Introductionmentioning

confidence: 99%

Spectrum sensing based on adversarial transfer learning

Miao

Jing

et al. 2022

IET Communications

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Recently, deep learning (DL) based spectrum sensing (SS) has drawn much attention due to its better capacity of feature extraction and superb performance. However, the model robustness of the DL based scheme is limited by reason of the dynamic radio environment, leading to the floating of sensing performance. Motivated by this, adversarial transfer learning is applied to SS here, where the model is pre‐trained at the central node firstly and fine‐tuned at the local nodes. More specifically, a 2D dataset of the observed signal is constructed under various signal‐to‐noise‐ratio (SNRs) and a convolution neural network (CNN) model is designed. Then a part of samples with various SNRs in the constructed dataset are employed to pre‐train the proposed CNN model. After that, the pre‐trained CNN model is distributed to local nodes with different SNRs and the pre‐trained CNN model is fine‐tuned. The proposed CNN model is pre‐trained based on the samples under various SNRs, resulting in its stronger adaptability at the local node. The simulation experiments validate the effectiveness of the proposed scheme.

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Rate Region and Achievable Rates of Full-Duplex Cognitive Radio NOMA Channels Under Imperfect Spectrum Sensing

Ranjbar

Tran

Karacolak

et al. 2022

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Energy efficiency of full-duplex cognitive radio in low-power regimes under imperfect spectrum sensing

Cited by 5 publications

References 52 publications

Detection of Emulation Attacks in Cognitive Radio Networks using Heuristic Techniques

Detection of Emulation Attacks in Cognitive Radio Networks using Heuristic Techniques

Spectrum sensing based on adversarial transfer learning

Rate Region and Achievable Rates of Full-Duplex Cognitive Radio NOMA Channels Under Imperfect Spectrum Sensing

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