5G Cognitive Radio Networks Using Reliable Hybrid Deep Learning Based on Spectrum Sensing
Spectrum sensing is critical in allowing the cognitive radio network, which will be used in the next generation of wireless communication systems. Several approaches, including cyclostationary process, energy detectors, and matching filters, have been suggested over the course of several decades. Th...
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description | Spectrum sensing is critical in allowing the cognitive radio network, which will be used in the next generation of wireless communication systems. Several approaches, including cyclostationary process, energy detectors, and matching filters, have been suggested over the course of several decades. These strategies, on the other hand, have a number of disadvantages. Energy detectors have poor performance when the signal-to-noise ratio (SNR) is changing, cyclostationary detectors are very complicated, and matching filters need previous knowledge of the main user (PU) signals. Additionally, these strategies rely on thresholds under particular signal-noise model assumptions in addition to the thresholds, and as a result, the detection effectiveness of these techniques is wholly dependent on the accuracy of the sensor. In this way, one of the most sought-after difficulties among wireless researchers continues to be the development of a reliable and intelligent spectrum sensing technology. In contrast, multilayer learning models are not ideal for dealing with time-series data because of the large computational cost and high rate of misclassification associated with them. For this reason, the authors propose a hybrid combination of long short-term memory (LSTM) and extreme learning machines (ELM) to learn temporal features from spectral data and to exploit other environmental activity statistics such as energy, distance, and duty cycle duration for the improvement of sensing performance. The suggested system has been tested on a Raspberry Pi Model B+ and the GNU-radio experimental testbed, among other platforms. |
doi_str_mv | 10.1155/2022/1830497 |
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Several approaches, including cyclostationary process, energy detectors, and matching filters, have been suggested over the course of several decades. These strategies, on the other hand, have a number of disadvantages. Energy detectors have poor performance when the signal-to-noise ratio (SNR) is changing, cyclostationary detectors are very complicated, and matching filters need previous knowledge of the main user (PU) signals. Additionally, these strategies rely on thresholds under particular signal-noise model assumptions in addition to the thresholds, and as a result, the detection effectiveness of these techniques is wholly dependent on the accuracy of the sensor. In this way, one of the most sought-after difficulties among wireless researchers continues to be the development of a reliable and intelligent spectrum sensing technology. In contrast, multilayer learning models are not ideal for dealing with time-series data because of the large computational cost and high rate of misclassification associated with them. For this reason, the authors propose a hybrid combination of long short-term memory (LSTM) and extreme learning machines (ELM) to learn temporal features from spectral data and to exploit other environmental activity statistics such as energy, distance, and duty cycle duration for the improvement of sensing performance. The suggested system has been tested on a Raspberry Pi Model B+ and the GNU-radio experimental testbed, among other platforms.</description><identifier>ISSN: 1530-8669</identifier><identifier>EISSN: 1530-8677</identifier><identifier>DOI: 10.1155/2022/1830497</identifier><language>eng</language><publisher>Oxford: Hindawi</publisher><subject>Algorithms ; Artificial neural networks ; Cognitive radio ; Deep learning ; Detectors ; Eigenvalues ; Internet of Things ; Licenses ; Machine learning ; Matching ; Memory ; Multilayers ; Neural networks ; Radios ; Sensors ; Signal processing ; Signal to noise ratio ; Spectrum allocation ; Support vector machines ; Thresholds ; Wireless communication systems</subject><ispartof>Wireless communications and mobile computing, 2022-04, Vol.2022, p.1-17</ispartof><rights>Copyright © 2022 Vinodkumar Mohanakurup et al.</rights><rights>Copyright © 2022 Vinodkumar Mohanakurup et al. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c337t-8f4ef9f2a97385b851b27662dcae1191024e7f3f2fd9939d7c98d9f53b9944843</citedby><cites>FETCH-LOGICAL-c337t-8f4ef9f2a97385b851b27662dcae1191024e7f3f2fd9939d7c98d9f53b9944843</cites><orcidid>0000-0001-6201-4254 ; 0000-0001-9459-1312 ; 0000-0002-6069-6404</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><contributor>Sur, Samarendra Nath</contributor><contributor>Samarendra Nath Sur</contributor><creatorcontrib>Mohanakurup, Vinodkumar</creatorcontrib><creatorcontrib>Baghela, Vishwadeepak Singh</creatorcontrib><creatorcontrib>Kumar, Sarvesh</creatorcontrib><creatorcontrib>Srivastava, Prabhat Kumar</creatorcontrib><creatorcontrib>Doohan, Nitika Vats</creatorcontrib><creatorcontrib>Soni, Mukesh</creatorcontrib><creatorcontrib>Awal, Halifa</creatorcontrib><title>5G Cognitive Radio Networks Using Reliable Hybrid Deep Learning Based on Spectrum Sensing</title><title>Wireless communications and mobile computing</title><description>Spectrum sensing is critical in allowing the cognitive radio network, which will be used in the next generation of wireless communication systems. 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Several approaches, including cyclostationary process, energy detectors, and matching filters, have been suggested over the course of several decades. These strategies, on the other hand, have a number of disadvantages. Energy detectors have poor performance when the signal-to-noise ratio (SNR) is changing, cyclostationary detectors are very complicated, and matching filters need previous knowledge of the main user (PU) signals. Additionally, these strategies rely on thresholds under particular signal-noise model assumptions in addition to the thresholds, and as a result, the detection effectiveness of these techniques is wholly dependent on the accuracy of the sensor. In this way, one of the most sought-after difficulties among wireless researchers continues to be the development of a reliable and intelligent spectrum sensing technology. In contrast, multilayer learning models are not ideal for dealing with time-series data because of the large computational cost and high rate of misclassification associated with them. For this reason, the authors propose a hybrid combination of long short-term memory (LSTM) and extreme learning machines (ELM) to learn temporal features from spectral data and to exploit other environmental activity statistics such as energy, distance, and duty cycle duration for the improvement of sensing performance. The suggested system has been tested on a Raspberry Pi Model B+ and the GNU-radio experimental testbed, among other platforms.</abstract><cop>Oxford</cop><pub>Hindawi</pub><doi>10.1155/2022/1830497</doi><tpages>17</tpages><orcidid>https://orcid.org/0000-0001-6201-4254</orcidid><orcidid>https://orcid.org/0000-0001-9459-1312</orcidid><orcidid>https://orcid.org/0000-0002-6069-6404</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Algorithms Artificial neural networks Cognitive radio Deep learning Detectors Eigenvalues Internet of Things Licenses Machine learning Matching Memory Multilayers Neural networks Radios Sensors Signal processing Signal to noise ratio Spectrum allocation Support vector machines Thresholds Wireless communication systems |
title | 5G Cognitive Radio Networks Using Reliable Hybrid Deep Learning Based on Spectrum Sensing |
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