Computation‐constrained spectrum sensing in IoT‐based scenarios

Mu, Junsheng; Xie, Dong; Huang, Hai; Jing, Xiaojun

doi:10.1049/iet-com.2019.1367

Cited by 4 publications

(2 citation statements)

References 29 publications

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“…Düzenli et al [21] used a dynamic programming method to accelerate the calculation of the least square method, and developed a dynamic spectrum sensing strategy. Mu et al [22] gave two spectrum sensing models with constraints, and respectively calculated the optimal sensing operation under the constraints. Muhammad et al [23] proposed an intelligent adaptive spectrum sensing method that minimized the impact of minimal denial of service interference.…”

Section: Related Workmentioning

confidence: 99%

Securing Radio Resources Allocation With Deep Reinforcement Learning for IoE Services in Next-Generation Wireless Networks

Peng

Xue

Zhu

et al. 2022

IEEE Trans. Netw. Sci. Eng.

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The next generation wireless network (NGWN) is undergoing an unprecedented revolution, in which trillions of machines, people, and objects are interconnected to realize the Internet of Everything (IoE). with the emergence of IoE services such as virtual reality, augmented reality, and industrial 5G, the scarcity of radio resources becomes more serious. Moreover, there are hidden dangers of untrusted terminals accessing the system and illegally manipulating interconnected devices. To tackle these challenges, this paper proposes a securing radio resources allocation scheme with Deep Reinforcement Learning for IoE services in NGWN. First, the solution uses a BP neural network based on multi-feature optimized Firefly Algorithm (FA) for spectrum prediction, thereby improving the prediction accuracy and avoiding interference between unauthorized and authorized users with efficient radio utilization. Then, a spectrum sensing method based on deep reinforcement learning is proposed to identify the untrusted users in system while fusing the sensing results, to enhance the security of the cooperative process and the detection accuracy of spectrum holes. Extensive simulation results show that the proposal is superior to the traditional solutions in terms of prediction accuracy, spectrum utilization and energy consumption, and is suitable for deployment in future wireless systems.

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Section: Related Workmentioning

confidence: 99%

Securing Radio Resources Allocation With Deep Reinforcement Learning for IoE Services in Next-Generation Wireless Networks

Peng

Xue

Zhu

et al. 2022

IEEE Trans. Netw. Sci. Eng.

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“…With the in-depth integration of artificial intelligence (AI), Internet of Things (IoT) and wireless communications, wireless devices are growing exponentially, resulting in an increasing shortage of spectrum resources [1][2][3]. According to the report of Federal Communications Commission (FCC), the spectrum utilization of current registered frequency bands is very low [4].…”

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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UFed-GAN: Secure Federated Learning over Wireless Sensor Networks with Unlabeled Data

Wijesinghe,

Zhang,

et al. 2024

2024 IEEE International Conference on Communications Workshops (ICC Workshops)

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Computation‐constrained spectrum sensing in IoT‐based scenarios

Cited by 4 publications

References 29 publications

Securing Radio Resources Allocation With Deep Reinforcement Learning for IoE Services in Next-Generation Wireless Networks

Securing Radio Resources Allocation With Deep Reinforcement Learning for IoE Services in Next-Generation Wireless Networks

Spectrum sensing based on adversarial transfer learning

UFed-GAN: Secure Federated Learning over Wireless Sensor Networks with Unlabeled Data

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