Adaptive cooperative sensing in cognitive radio networks with ensemble model for primary user detection

Abstract

Opportunistic spectrum sharing ability enables higher spectrum utilization in cognitive radio networks. Detecting the presence of primary user in the network is the most important functionality in cognitive radio network as the cognitive users cannot use the spectrum with interference to primary users. Most solutions proposed for primary user detection suffer from hidden terminal problem resulting from multipath fading and shadow effects. The work focus on Rayleigh and Nakagami fading channel with comparable nonfading AWGN channel in cognitive radio. An ensemble model to detect the presence of primary user with high confidence is proposed in this work. The approach is based on training machine learning models with energy vectors in presence and absence of primary users. The trained model is then used to predict the primary user based on the energy vector.

CapsNet‐based computing in cognitive communications

“…To enhance depth information for stereo saliency examination, this model adventures depth information from three angles. Further, the objective and subjective outcomes are introduced, exhibiting that the proposed approach gives half piece rate investment funds in correlation with JMVDC simulcast. “Adaptive Cooperative Sensing with Ensemble model for Primary User Detection in Cognitive Radio Networks”—Authored by Prasad , Venkata; Rao , Trinath Pollipalli: This research work [7] focuses on Rayleigh and Nakagami fading channel with comparable nonfading AWGN channel in CR. An ensemble model to detect the presence of PU with high confidence is proposed in this work.…”

A Novel Prediction Model for Malicious Users Detection and Spectrum Sensing Based on Stacking and Deep Learning

“…For the more complicated tasks, usually deep learning is used, which is a subset of the machine learning field, and consists of layered structures known as artificial neural networks inspired by the human brain [13]. Examples of machine learning-based techniques are proposed in [14][15][16][17][18][19][20][21] and summarized in Table 1. For MUs detection, the authors in [14] implemented a new support vector machine (SVM) algorithm to separate MUs from SUs under a three class hypothesis.…”

“…For PU spectrum sensing, the authors in [18] performed a comparative study of different machine learning techniques; namely, random forest, SVM, KNN, DCT, NB, and LR, using the energy statistic as the input feature vector, to investigate their efficiency for the spectrum sensing process. The authors in [19] proposed an adaptive cooperative sensing technique using a weighted ensemble model. This model used three different feature vectors: energy feature, wavelet feature, and SNR feature.…”