ConvLSTM based Spectrum Sensing Scheme for Cognitive Radio Networks

Abstract

Spectrum sensing is the key component for Cognitive Radio (CR) as it helps in finding the spectrum holes present on the spectrum. Now a days, Deep learning (DL) models shows promising result in finding the spectrum holes specifically CNN and LSTM network. It is made up of numerous neural layers that represent data at different abstract levels and has the ability to learn large signal data semantically at a high level, which can be a possible solution to all such problems. As a key technology of cognitive radio, spectrum sensing has an irreplaceable position. In this paper, we proposed a parallel CNN-LSTM network based DL model called (ConvLSTM) for spectrum sensing. The proposed model in the given scenario with Adam optimization shows the better performance for spectrum sensing when compared based on probability of detection (P_d), probability of false alarm (P_fa), PU transmission power and Signal-to-Noise ratio (SNR) with existing models.