Deep learning application for sensing available spectrum for cognitive radio: An ECRNN approach

Goyal, S. B.; Bedi, Pradeep; Kumar, Jugnesh; Varadarajan, Vijaykumar

doi:10.1007/s12083-021-01169-4

Cited by 25 publications

(7 citation statements)

References 28 publications

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“…When the information of the SN surpassed the specified range, a notification was sent to the clients insisting on the environmental setup being altered accordingly [24][25][26]. Energy is a critical issue in WSNs and the IoT, specifically when deployed in smart city applications and this was discussed briefly in [27][28][29][30].…”

Section: Related Workmentioning

confidence: 99%

Secure Routing-Based Energy Optimization for IoT Application with Heterogeneous Wireless Sensor Networks

Nagaraju¹,

Venkatesan²,

Kalaivani

et al. 2022

Energies

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Wireless sensor networks (WSNs) and the Internet of Things (IoT) are increasingly making an impact in a wide range of domain-specific applications. In IoT-integrated WSNs, nodes generally function with limited battery units and, hence, energy efficiency is considered as the main design challenge. For homogeneous WSNs, several routing techniques based on clusters are available, but only a few of them are focused on energy-efficient heterogeneous WSNs (HWSNs). However, security provisioning in end-to-end communication is the main design challenge in HWSNs. This research work presents an energy optimizing secure routing scheme for IoT application in heterogeneous WSNs. In our proposed scheme, secure routing is established for confidential data of the IoT through sensor nodes with heterogeneous energy using the multipath link routing protocol (MLRP). After establishing the secure routing, the energy and network lifetime is improved using the hybrid-based TEEN (H-TEEN) protocol, which also has load balancing capacity. Furthermore, the data storage capacity is improved using the ubiquitous data storage protocol (U-DSP). This routing protocol has been implemented and compared with two other existing routing protocols, and it shows an improvement in performance parameters such as throughput, energy efficiency, end-to-end delay, network lifetime and data storage capacity.

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Section: Related Workmentioning

confidence: 99%

Secure Routing-Based Energy Optimization for IoT Application with Heterogeneous Wireless Sensor Networks

Nagaraju¹,

Venkatesan²,

Kalaivani

et al. 2022

Energies

Self Cite

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“…https://www.indjst.org/ Many expert features and decision statistics are developed analytically using this simplified formula, although the real-world relationship often looks more like that given by equation (6).…”

Section: Deep Learning Based Modulation Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…CR systems face the challenge of detecting a radio signal's modulation type in order to determine what type of communication scheme and transmitter is there. Convolutional neural networks (CNNs) have recently been used to the job of radio modulation identification in order to introduce the notion of deep learning(5)(6)(7)(8) .There has been a lot of recent pioneering work on the use of deep learning to extract signal characteristics and demonstrate the feasibility of utilizing convolutional neural networks (CNNs) for modulation classification. Various techniques like implementing ANN with multiple detectors' test statistics (TSs)(9) , Comparison of DL-based estimator with BEM-LMMSE estimator(10) , Doubly Selective Fading Channel Estimation Using Deep Learning(11) , and combining various CNNs to increase the Modulation Classification accuracy(12) has been proposed.…”

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confidence: 99%

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Over-the-Air Modulation Classification using Deep Learning in Fading Channels for Cognitive Radio

Arjun¹,

Surekha²

2021

IJST

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Background/Objectives: The ability to recognize the type of modulation is a critical function of Cognitive Radio. The objective of this study is to increase the modulation classification efficiency in Over-The-Air (OTA) signals by utilizing channel characteristics that are strong. Methods: In this work, we demonstrate how to classify Over-The-Air modulation using a deep learning technique under various fading channels simulating real-time data. The network recognizes eight different digital modulation schemes and three different analogue modulation methods. Each modulation scheme will consist of 10,000 frames with 1024 samples per frame and a sampling rate of 200 kHz. Each sample will pass through fading channels prior to training, with 80% of samples are for training, 10% for validation, and 10% for testing. Six convolutional layers and one fully linked layer comprise our network. The final convolution layer is followed by a batch normalization layer, an activation layer utilizing rectified linear units (ReLUs), and a maximum pooling layer. As a result, the final convolution layer contains soft-max activation instead of the maximum pooling layer. Findings: Modulation categorization OTA is done with two separate ADALM-PLUTO SDRs working in various channel configurations. Network-I has a forecast accuracy of 91.4 percent using 12 Mini-Batch Size and 256 Epochs, whereas Network-II has a prediction accuracy of 95.3 percent using 24 Mini-Batch Size and 128 Epochs. There are a number of ways in which SDR technology can aid to make computer-generated data more realistic, such as adopting alternative channel models. Novelty: Using Software Defined Radio hardware; the same network was used to analyze various fading situations, such as Rayleigh, Rician or Lognormal distributions, and to optimize the network topology by adjusting hyper-parameters to increase accuracy.

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“…In CRN, the spectrum utilization method allows unlicensed users or secondary users (SUs) to speculatively accomplish spectrum holes or cognitive white spaces that are not used by primary users (PUs) or licensed users. For spectrum sensing, many researchers have proposed machine learning (ML) and deep learning (DL) algorithms to enhance the capability of spectrum sensing [ 5 , 6 , 7 , 8 ]. However, the channel availability depends on the condition of PUs’ activity.…”

Section: Introductionmentioning

confidence: 99%

Secure Spectrum Access, Routing, and Hybrid Beamforming in an Edge-Enabled mmWave Massive MIMO CRN-Based Internet of Connected Vehicle (IoCV) Environments

Pari

Jaisankar

2022

Sensors

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A cognitive radio network (CRN) is integrated with the Internet of Connected Vehicles (IoCV) in order to address spectrum scarcity and communication reliability issues. However, it is limited, possessing less throughput, a low packet delivery ratio, high latency, and high mobility in the spectrum. In this research study, the existing issues are addressed by proposing a 6G cognitive radio network–Internet of connected vehicles (6GCRN–IoCV) approach. Initially, all the entities such as secondary users (SUs), primary users (PUs), and pedestrians are authenticated in blockchain to ensure security. The edge-assisted roadside units (ERSU) initiate clustering only for authenticated SUs using the improved DBSCAN algorithm in consideration of several metrics. The ERSU then generates an intersection-aware map using the spatial and temporal-based logistic regression algorithm (STLR) to reduce collisions in the intersection. The spectrum utilization is improved by performing spectrum sensing in which all the SUs involved in spectrum sensing use lightweight convolutional neural networks (Lite-CNN) in consideration of several metrics and provide the sensing report to the fusion center (FC) in an encrypted manner to reduce the spectrum scarcity and security issues. The communications between the SUs are necessary to avoid risks in the IoCV environment. Hence, optimal routing is performed using the Dingo Optimization Algorithm (DOA), which increases throughput and packet delivery ratio. Finally, communication reliability is enhanced by performing hybrid beamforming, and this exploits the multi-agent-based categorical Deep-Q Network (categorical DQN), which increases spectral efficiency based on its adaptive intelligent behavior. The proposed study is simulated using the SUMO and OMNeT++ simulation tools and the performances are validated with existing works using several performance metrics. The result of the simulation shows that the proposed work performs better than the existing approaches.

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Deep learning application for sensing available spectrum for cognitive radio: An ECRNN approach

Cited by 25 publications

References 28 publications

Secure Routing-Based Energy Optimization for IoT Application with Heterogeneous Wireless Sensor Networks

Secure Routing-Based Energy Optimization for IoT Application with Heterogeneous Wireless Sensor Networks

Over-the-Air Modulation Classification using Deep Learning in Fading Channels for Cognitive Radio

Secure Spectrum Access, Routing, and Hybrid Beamforming in an Edge-Enabled mmWave Massive MIMO CRN-Based Internet of Connected Vehicle (IoCV) Environments

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