A cooperative wideband spectrum sensing framework in the frequency domain for resource allocation with 5G-NR

Zhao, Yingxin; Wang, Changfeng; Hu, Man; Wu, Hong; Lin, F.-L.C.; Liu, Zhiyang

doi:10.1016/j.measurement.2022.110756

Cited by 3 publications

(2 citation statements)

References 29 publications

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“…Zhao et al [83] propose to construct a dual-threshold CSS framework for a 5G network. The proposed algorithm utilizes the presence of a cyclic prefix in the 5G OFDM signal which results in autocorrelation in the PU signal.…”

Section: G Communications and Spectrum Sensingmentioning

confidence: 99%

A review of spectrum sensing in modern cognitive radio networks

Muzaffar,

Sharqi

2023

Telecommun Syst

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Cognitive radio network (CRN) is a pioneering technology that was developed to improve efficiency in spectrum utilization. It provides the secondary users with the privilege to transmit on the licensed parts of the spectrum if the licensed user is not utilizing it. The cognitive radio must, however, relinquish the spectrum when the primary user decides to reoccupy it. By exploiting the unused portion of the spectrum, a cognitive radio helps in making the use of the radio spectrum more efficient. Furthermore, the most important capability that a cognitive radio (CR) must possess is spectrum sensing. A CR must be able to correctly determine the status of the target spectrum with the help of spectrum sensing. This is a very challenging task and several methods have been investigated over the years. In this work, the state of the art of different spectrum sensing techniques for a variety of CRNs is presented. Both conventional and modern spectrum sensing techniques for different types of primary user signals are discussed in this work for Narrowband and Wideband signals. Legacy techniques such as energy detection are most commonly used due to their simplicity in implementation. However, this comes at the cost of poor performance at low SNR (signal-to-noise ratio) values. This issue is countered by methods that use statistical information of the primary signal to make a more informed decision on spectrum occupancy. Several techniques that make use of the power of machine learning algorithms are also discussed which show clear improvement in performance. The primary challenge in such techniques is selection of the best features. The most commonly used features are also discussed. Furthermore, spectrum sensing techniques that consider the 5G signal as the primary user signal of the network are discussed. It is observed that there is a significant need for research in additional spectrum sensing techniques for 5G cognitive radio networks.

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Section: G Communications and Spectrum Sensingmentioning

confidence: 99%

A review of spectrum sensing in modern cognitive radio networks

Muzaffar,

Sharqi

2023

Telecommun Syst

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“…Among the four steps, spectrum sensing (SS) is one of the essential components of cognitive radio, which is the core technology and prerequisite for realizing CR applications and constructing cognitive radio networks [6] and is also the focus of this paper. Spectrumsensing technology enhances spectrum use efficiency by continuously monitoring and analyzing the radio spectrum.…”

Section: Introductionmentioning

confidence: 99%

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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