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5G Cognitive Radio Networks Using Reliable Hybrid Deep Learning Based on Spectrum Sensing
Author(s) -
Vinodkumar Mohanakurup,
Vishwadeepak Singh Baghela,
Sarvesh Kumar,
Prabhat Kumar Srivastava,
Nitika Vats Doohan,
Mukesh Soni,
Awal Halifa
Publication year - 2022
Publication title -
wireless communications and mobile computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.42
H-Index - 64
eISSN - 1530-8677
pISSN - 1530-8669
DOI - 10.1155/2022/1830497
Subject(s) - computer science , cognitive radio , cyclostationary process , detector , testbed , wireless , false alarm , noise (video) , duty cycle , energy (signal processing) , real time computing , artificial intelligence , machine learning , telecommunications , channel (broadcasting) , computer network , electrical engineering , statistics , image (mathematics) , mathematics , engineering , voltage
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.

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