5G Cognitive Radio Networks Using Reliable Hybrid Deep Learning Based on Spectrum Sensing

被引:11
|
作者
Mohanakurup, Vinodkumar [1 ]
Baghela, Vishwadeepak Singh [2 ]
Kumar, Sarvesh [3 ]
Srivastava, Prabhat Kumar [4 ]
Doohan, Nitika Vats [5 ]
Soni, Mukesh
Awal, Halifa [6 ]
机构
[1] Bell Canada, Mississauga, ON, Canada
[2] Galgotias Univ, Sch Comp Sci & Engn, Greater Noida, India
[3] Babu Banarasi Das Univ, Dept Comp Sci & Engn, Lucknow, Uttar Pradesh, India
[4] Quantum Univ, Dept Comp Sci & Engn, Roorkee, Uttarakhand, India
[5] Medi Caps Univ, Dept Comp Sci & Engn, Indore, India
[6] Tamale Tech Univ, Dept Elect & Elect Engn, Tamale, Ghana
关键词
ENERGY DETECTION; MACHINE; CNN;
D O I
10.1155/2022/1830497
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
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页数:17
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