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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Veröffentlicht in:Wireless communications and mobile computing 2022-04, Vol.2022, p.1-17
Hauptverfasser: Mohanakurup, Vinodkumar, Baghela, Vishwadeepak Singh, Kumar, Sarvesh, Srivastava, Prabhat Kumar, Doohan, Nitika Vats, Soni, Mukesh, Awal, Halifa
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container_end_page 17
container_issue
container_start_page 1
container_title Wireless communications and mobile computing
container_volume 2022
creator Mohanakurup, Vinodkumar
Baghela, Vishwadeepak Singh
Kumar, Sarvesh
Srivastava, Prabhat Kumar
Doohan, Nitika Vats
Soni, Mukesh
Awal, Halifa
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. 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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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