A machine learning‐based compressive spectrum sensing in 5G networks using cognitive radio networks

Perumal, Ramakrishnan; Nagarajan, Sathish Kumar

doi:10.1002/dac.5302

Cited by 6 publications

(2 citation statements)

References 26 publications

(37 reference statements)

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“…Cognitive radio networks (CRNs) have developed viable ways to allocate the necessary spectrum to customers in an intelligent way as a result of the rising scarcity of spectrum for this equipment. Utilizing a potent CNN classifier (Perumal & Nagarajan, 2022), the compressive sensing-based cyclo-stationary feature detection approach is used to determine whether or not PU activity is present. The ideal detection has been redesigned to reduce the potential of mistakes while improving detection probability and MSE.…”

Section: Sensing-based Applicationsmentioning

confidence: 99%

Evolution toward intelligent communications: Impact of deep learning applications on the future of 6G technology

Abd Elaziz,

Al‐qaness,

Dahou

et al. 2023

WIREs Data Min & Knowl

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The sixth generation (6G) represents the next evolution in wireless communication technology and is currently under research and development. It is expected to deliver faster speeds, reduced latency, and greater capacity compared to the current 5G wireless technology. 6G is envisioned as a technology capable of establishing a fully data‐driven network, proficient in analyzing and optimizing end‐to‐end behavior and handling massive volumes of real‐time data at rates of up to terabits per second (Tb/s). Moreover, 6G is designed to accommodate an average of 1000+ substantial connections per person over the course of a decade. The concept of a data‐driven network introduces a new service paradigm, which offers fresh opportunities for applications within 6G wireless communication and network design in the future. This paper aims to provide a survey of existing applications of 6G that are based on deep learning techniques. It also explores the potential, essential technologies, scenarios, challenges, and related topics associated with 6G. These aspects are crucial for meeting the requirements for the development of future intelligent networks. Furthermore, this work delves into various research gaps between deep learning and 6G that remain unexplored. Different potential deep learning applications for 6G networks, including privacy, security, environmentally friendly communication, sustainability, and various wireless applications, are discussed. Additionally, we shed light on the challenges and future trends in this field.This article is categorized under: Technologies > Computational Intelligence Fundamental Concepts of Data and Knowledge > Explainable AI Technologies > Machine Learning

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Section: Sensing-based Applicationsmentioning

confidence: 99%

Evolution toward intelligent communications: Impact of deep learning applications on the future of 6G technology

Abd Elaziz,

Al‐qaness,

Dahou

et al. 2023

WIREs Data Min & Knowl

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“…Perumal and Nagarajan [78] propose a machine learningbased compressive sampling spectrum sensing approach in a 5G CRN. A Convolutional Neural Network (CNN) is trained using cyclostationary features of the dataset which are obtained from the carrier signal structures using the time delay and cyclic frequency.…”

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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