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

Abstract

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.

Spectrum sensing-focused cognitive radio network for 5G revolution

“…The deterministic and empirical methods utilized to predict path loss did not produce the best possible outcomes. Support vector regression and radial basis function (RBF) models were evaluated to increase the precision of path loss predictions in the environment (Mohanakurup et al, 2022). The accomplishment of the visible light communication (VLC) system is severely hindered because of the non-linearity that is caused by lightemitting diodes (LEDs), which interferes with the regular transmission and reception processes.…”