Cluster mechanism for sensing data report using robust collaborative distributed spectrum sensing

Mokhtar, Rania A.; Saeed, Rashid A.; Alhumyani, Hesham; Khayyat, Mashael; Abdel‐Khalek, S.

doi:10.1007/s10586-021-03363-8

Cited by 10 publications

(6 citation statements)

References 60 publications

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“…Knowing a priori information about the signal, higher SNR, and higher phase synchronization. [37][38][39][40][41][42][43] Cooperative spectrum sensing…”

Section: Performance Comparison Of Conventional Methods and Deep-lear...mentioning

confidence: 99%

“…Although compared to centralized cooperative sensing, distributed cooperative sensing does not rely on data fusion centers to make cooperative decisions; local cognitive nodes need to interact more regularly with local sensing information, which puts higher demands on control channel resources. In order to improve the reliability of robust cooperative spectrum sensing in dense network environments, a current data fusion scheme based on clustering algorithms and distributed sensing by Rania A. Mokhtar et al [42] is suitable for environments with a high degree of relay channel fading and is designed to distribute the clusters and select the fusion nodes (FNs) in two phases. The results show that there is an improvement of 0.42 in the error through the two processes; moreover, the detection sensitivity is also improved to 0.95.…”

Section: Distributed Cooperative Spectrum Detectionmentioning

confidence: 99%

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A Review of Research on Spectrum Sensing Based on Deep Learning

Zhang,

Luo

2023

Electronics

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In recent years, with the rapid development in wireless communication and 5G networks, the rapid growth in mobile users has been accompanied by an increasing demand for the electromagnetic spectrum. The birth of cognitive radio and its spectrum-sensing technology provides hope for solving the problem of low utilization of the wireless spectrum. Artificial intelligence (AI) has been widely discussed globally. Deep learning technology, known for its strong learning ability and adaptability, plays a significant role in this field. Moreover, integrating deep learning with wireless communication technology has become a prominent research direction in recent years. The research objective of this paper is to summarize the algorithm of cognitive radio spectrum-sensing technology combined with deep learning technology. To review the advantages of deep-learning-based spectrum-sensing algorithms, this paper first introduces the traditional spectrum-sensing methods. It summarizes and compares the advantages and disadvantages of each method. It then describes the application of deep learning algorithms in spectrum sensing and focuses on the typical deep-neural-network-based sensing methods. Then, the existing deep-learning-based cooperative spectrum-sensing methods are summarized. Finally, the deep learning spectrum-sensing methods are discussed, along with challenges in the field and future research directions.

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“…Knowing a priori information about the signal, higher SNR, and higher phase synchronization. [37][38][39][40][41][42][43] Cooperative spectrum sensing…”

Section: Performance Comparison Of Conventional Methods and Deep-lear...mentioning

confidence: 99%

Section: Distributed Cooperative Spectrum Detectionmentioning

confidence: 99%

A Review of Research on Spectrum Sensing Based on Deep Learning

Zhang,

Luo

2023

Electronics

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“…As known, coding based on the classical and quantum theory of information has many applications on the Internet of Things [ 22 – 26 ]. In quantum computing, three-qubit codes can be used to correct the single-bit flip error and single-phase error at a time.…”

Section: Digital System Design For Quantum Error Correctionmentioning

confidence: 99%

Digital System Design for Quantum Error Correction Codes

Khalifa

Sharif

Saeed

et al. 2021

Contrast Media & Molecular Imaging

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Quantum computing is a computer development technology that uses quantum mechanics to perform the operations of data and information. It is an advanced technology, yet the quantum channel is used to transmit the quantum information which is sensitive to the environment interaction. Quantum error correction is a hybrid between quantum mechanics and the classical theory of error-correcting codes that are concerned with the fundamental problem of communication, and/or information storage, in the presence of noise. The interruption made by the interaction makes transmission error during the quantum channel qubit. Hence, a quantum error correction code is needed to protect the qubit from errors that can be caused by decoherence and other quantum noise. In this paper, the digital system design of the quantum error correction code is discussed. Three designs used qubit codes, and nine-qubit codes were explained. The systems were designed and configured for encoding and decoding nine-qubit error correction codes. For comparison, a modified circuit is also designed by adding Hadamard gates.

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Section: Introductionmentioning

confidence: 99%

“…To enhance the reliability of robust cooperative spectrum sensing, the article proposed a new data fusion scheme based on clustering algorithm and distributed detection, in addition to an adapted threshold based on controlled false alarm probability. 20 In cognitive radio networks, spectrum sensing itself consumes a lot of energy, and ineffective spectrum sensing increases energy consumption. Therefore, there is a need to improve spectrum sensing to increase energy efficiency.…”

Section: Introductionmentioning

confidence: 99%

Energy-efficient and intelligent cooperative spectrum sensing algorithm in cognitive radio networks

Huang

Yin

2022

International Journal of Distributed Sensor Networks

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Green communication is the demand of current and future wireless communication. As the next-generation communication network, cognitive radio network also needs to meet the requirements of green communication. Therefore, improving energy efficiency is an inevitable requirement for the development of cognitive radio networks. However, there is a need to compromise sensing performance while improving energy efficiency. To take into account the two important indicators of sensing performance and energy efficiency, a grouping algorithm is proposed in this article, which can effectively improve the energy efficiency while improving the spectrum sensing performance. The algorithm obtains the initial value of the reliability of the nodes through training, and sorts them according to the highest reliability value, then selects an even number of nodes with the highest reliability value, and divides the selected nodes into two groups, and the two groups of nodes take turns in Alternate work. At this time, other nodes not participating in cooperative spectrum sensing are in a silent state, waiting for the instruction of the fusion center. The experimental results show that compared with the traditional algorithm, the proposed algorithm has a great improvement in the two indicators of sensing performance and energy efficiency.

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Cluster mechanism for sensing data report using robust collaborative distributed spectrum sensing

Cited by 10 publications

References 60 publications

A Review of Research on Spectrum Sensing Based on Deep Learning

A Review of Research on Spectrum Sensing Based on Deep Learning

Digital System Design for Quantum Error Correction Codes

Energy-efficient and intelligent cooperative spectrum sensing algorithm in cognitive radio networks

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