Deep Stacked CNN-LSTM (DS-CNN-LSTM) based Spectrum Sensing in Cognitive Radio

The multidimensionality of spectrum sensing, the intrinsic complexity of its dependence, and the unpredictability associated with spectrum data all contribute to the difficulty of the task. The network of cognitive radio (CR) is comprised of both primary and secondary users inside its network. The SUs that are part of the CR network are able to identify the spectrum band and access white space in an opportunistic manner. Enhancing spectrum efficiency may be accomplished by using white spaces. This study presents a Deep Stacked CNN-LSTM (DS-CNN-LSTM)-based spectrum sensing strategy that learns implicit features from spectrum data, such as temporal correlation. This approach is based on the research that we have conducted. The effectiveness of the recommended method is shown by a sufficient number of simulations, and the results of the simulations demonstrate that it outperforms the current state of the art in terms of detection probability and classification accuracy. A comparison is made between the most cutting-edge spectrum sensing approaches and the DS-CNN-LSTM method that has been recommended. The results of the experiments indicate that the proposed methods improve detection performance and classification accuracy even when the signal-to-noise ratio is low. As we can see, the improvement that was achieved comes at the price of a longer amount of time spent on training and a little increase in the amount of time spent on execution

Automatic modulation classification using interacting multiple model - Kalman filter for channel estimation

YesA rigorous model for automatic modulation classification (AMC) in cognitive radio (CR) systems is proposed in this paper. This is achieved by exploiting the Kalman filter (KF) integrated with an adaptive interacting multiple model (IMM) for resilient estimation of the channel state information (CSI). A novel approach is proposed, in adding up the squareroot singular values (SRSV) of the decomposed channel using the singular value decompositions (SVD) algorithm. This new scheme, termed Frobenius eigenmode transmission (FET), is chiefly intended to maintain the total power of all individual effective eigenmodes, as opposed to keeping only the dominant one. The analysis is applied over multiple-input multiple-output (MIMO) antennas in combination with a Rayleigh fading channel using a quasi likelihood ratio test (QLRT) algorithm for AMC. The expectation-maximization (EM) is employed for recursive computation of the underlying estimation and classification algorithms. Novel simulations demonstrate the advantages of the combined IMM-KF structure when compared to the perfectly known channel and maximum likelihood estimate (MLE), in terms of achieving the targeted optimal performance with the desirable benefit of less computational complexity loads