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Author

R. Sanju

Bio: R. Sanju is an academic researcher. The author has contributed to research in topics: Cognitive radio & Artificial neural network. The author has an hindex of 1, co-authored 1 publications receiving 2 citations.

Papers
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Proceedings ArticleDOI
01 Jul 2017
TL;DR: This work focuses the implementation of neural network by means of Levenberg-Maquardt algorithm in case of an energy detector in single and multi channel to solve a set of objective functions with minimum iterations and increases the efficiency of cognitive radio network (CRN).
Abstract: Cognitive Radio is the prime key for spectrum shortage and used to detect the unused spectrum by a way of multidimensional spectrum sensing concept. Though Spectrum sensing applies a huge load of energy, it can be shortened by utilizing different artificial neural network methods for determining proper spectrum vacancy. This work focuses the implementation of neural network by means of Levenberg-Maquardt algorithm in case of an energy detector in single and multi channel. It is used to solve a set of objective functions with minimum iterations and increases the efficiency of cognitive radio network (CRN).

3 citations


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Book ChapterDOI
01 Jan 2019
TL;DR: This chapter undertake a comprehensive analysis of 30 peer-reviewed scientific publications collated from 2017 to 2018 April that examine cognitive radio networks to identify practical solutions proposed to overcome critical challenges in this field.
Abstract: Cognitive radio technology (CRNs) will be the fundamental driving force behind the next generation (5G) mobile communication systems as it provides the optimal solution for the problem of spectrum scarcity via dynamic spectrum usage. The CRNs, however, pose several key challenges such as network management, spectrum allocation, and access, energy efficiency, interference, cost, spectrum sensing, security, and quality of service (QoS). In this chapter, the authors undertake a comprehensive analysis of 30 peer-reviewed scientific publications collated from 2017 to 2018 April that examine cognitive radio networks to identify practical solutions proposed to overcome critical challenges in this field. Nine distinct challenges were considered: network management, spectrum allocation, and access, energy efficiency, interference, cost, spectrum sensing, security, and QoS. The analysis demonstrates that the majority of research work related to CRN focuses on approaches to improve network management and, specifically, optimization of networks.
Journal ArticleDOI
24 Jan 2023-Sensors
TL;DR: In this paper , the authors used GRU in CRN to train and test the dataset of spectrum sensing results and achieved a high testing accuracy of 82.45%, training accuracy of 80.99% and detection probability of 1 is achieved at 65 epochs in this proposed work.
Abstract: Cognitive radio networks are vulnerable to numerous threats during spectrum sensing. Different approaches can be used to lessen these attacks as the malicious users degrade the performance of the network. The cutting-edge technologies of machine learning and deep learning step into cognitive radio networks (CRN) to detect network problems. Several studies have been conducted utilising various deep learning and machine learning methods. However, only a small number of analyses have used gated recurrent units (GRU), and that too in software defined networks, but these are seldom used in CRN. In this paper, we used GRU in CRN to train and test the dataset of spectrum sensing results. One of the deep learning models with less complexity and more effectiveness for small datasets is GRU, the lightest variant of the LSTM. The support vector machine (SVM) classifier is employed in this study’s output layer to distinguish between authorised users and malicious users in cognitive radio network. The novelty of this paper is the application of combined models of GRU and SVM in cognitive radio networks. A high testing accuracy of 82.45%, training accuracy of 80.99% and detection probability of 1 is achieved at 65 epochs in this proposed work.