Spectrum Sensing and Resource Allocation for Proficient Transmission in Cognitive Radio with 5G

Meena, M.; Rajendran, V.

doi:10.1080/03772063.2019.1672585

Cited by 15 publications

(7 citation statements)

References 32 publications

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“…A scheme for imbalanced data using a weighted Gini index is presented in [19]. A CR system for efficient spectrum utilization is modeled in [20,21].…”

Section: Related Workmentioning

confidence: 99%

Efficient Centralized Cooperative Spectrum Sensing Techniques for Cognitive Networks

Gnanasivam¹,

Bharathy²,

Rajendran³

et al. 2023

Computer Systems Science and Engineering

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Wireless Communication is a system for communicating information from one point to other, without utilizing any connections like wire, cable, or other physical medium. Cognitive Radio (CR) based systems and networks are a revolutionary new perception in wireless communications. Spectrum sensing is a vital task of CR to avert destructive intrusion with licensed primary or main users and discover the accessible spectrum for the efficient utilization of the spectrum. Centralized Cooperative Spectrum Sensing (CSS) is a kind of spectrum sensing. Most of the test metrics designed till now for sensing the spectrum is produced by using the Sample Covariance Matrix (SCM) of the received signal. Some of the methods that use the SCM for the process of detection are Pietra-Ricci Index Detector (PRIDe), Hadamard Ratio (HR) detector, Gini Index Detector (GID), etc. This paper presents the simulation and comparative performance analysis of PRIDe with various other detectors like GID, HR, Arithmetic to Geometric Mean (AGM), Volume-based Detector number 1 (VD1), Maximumto-Minimum Eigenvalue Detection (MMED), and Generalized Likelihood Ratio Test (GLRT) using the MATLAB software. The PRIDe provides better performance in the presence of variations in the power of the signal and the noise power with less computational complexity.

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“…A scheme for imbalanced data using a weighted Gini index is presented in [19]. A CR system for efficient spectrum utilization is modeled in [20,21].…”

Section: Related Workmentioning

confidence: 99%

Efficient Centralized Cooperative Spectrum Sensing Techniques for Cognitive Networks

Gnanasivam¹,

Bharathy²,

Rajendran³

et al. 2023

Computer Systems Science and Engineering

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show abstract

“…Cognitive radio enabled sensor nodes have the ability to sense free channels (spectrum sensing) and change the transmission parameters accordingly (spectrum decision). Sensing the channel requires proper channel selection among the multiple channels inside the sensor network [22]. In proposed approach based on special requirement of nodes, channels can be assigned on fixed basis to some nodes or else routing algorithm makes a decision on selection of channels.…”

Section: Channel Selection Using Sensors Cognitive Knowledgementioning

confidence: 99%

A Cognitive Knowledged Energy-Efficient Path Selection Using Centroid and Ant-Colony Optimized Hybrid Protocol for WSN-Assisted IoT

Kumar

Bala

2022

Wireless Pers Commun

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In WSN-assisted IoT environment, the sensors are resource constrained. The energy, computing and storage resources of deployed sensors in the sensing area are limited. Clustering is the key method for saving energy in wireless sensor networks. A hybrid protocol named as an Energy Efficient Centroid-based Ant colony Optimization (EECAO) protocol is proposed in this paper to improve the performance of the sensor network in WSN-assisted IoT environments. The protocol uses the concept of centroid based clustering to gather the information of local clusters and ant colony optimization to relay that information to the base station. proposed hybrid protocol includes multiple clustering factors such as energy cost, channel consistency and cognitive sensor throughput to select cluster heads and a new distributed cluster formation for self-organizing deployed sensors. Selection of the super cluster head among the cluster heads is based on the energy centroid position for a defined coverage area. In EECAO protocol, the energy level of cognitive sensors is the key parameter for defining the position of centroid. To reduce the long-distance communication, path optimization between the super cluster heads and the base station is carried out using an ant routing model. Our simulation results indicate that EECAO protocol performs better when benchmarked against existing ETSP and EECRP protocols. The proposed hybrid protocol EECAO is well-suited for networks that requires long lifetime when the base station is placed at either center, border or outside the network.

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“…Cognitive radio (CR) is a type of sophisticated scientific communication, in which the radio spectrum uses are managed (Devaraj et al, 2022). Interference during spectrum sensing, detection, and transmission requires the use of orthogonal frequency division multiplexing (OFDM) based on the cognitive radio network cooperative spectrum awareness resource allocation (CRN-CSS) (Meena & Rajendran, 2022). Ultra-dense networks (UDNs) provide high-throughput high-performance and user-centric wireless access to 5G deployments.…”

Section: Introductionmentioning

confidence: 99%

Spectrum sensing-focused cognitive radio network for 5G revolution

Ali

2023

Front. Environ. Sci.

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The cognitive radio network (CRN), an instrumental part of the next-generation wireless communication systems, is mainly dependent on spectrum sensing to function properly. The radio spectrum can help in clean energy transition and load capacity factors by providing a more efficient and accurate spectrum utilization. By using it, the number of spectrums that is used for communication can be optimized, which can reduce the amount of energy consumed by the network. Additionally, 5G radio spectrum sensing can be used to identify and classify different types of signals, which can help reduce the amount of interference in the network and improve the efficiency of energy utilization. It can also allow for the digitization of clean energy infrastructure and facilitate better decision-making processes that take into account environmental impacts while optimizing energy use because of its efficient characteristics like non-linearity, detection, scalability, robustness, generalization, non-stationarity in wireless environments, dynamic entity, weighted sum of Gaussian functions centered at specific frequencies, and robustness against noise and interference. It can adapt to noise and interference by adjusting its parameters, and this allows accurate distinguishing between primary and secondary users in the wireless spectrum, which is why a radial basis function is a popular choice for spectrum sensing in 5G networks. Radial basis function networks (RBFNs) can work better in 5G spectrum sensing for better signal classification, dynamic adaptation, fast detection, faster decision-making, and improved noise and interference reduction. One of the most sought-after goals in the field of wireless research is the creation of spectrum-sensing technology that is dependable and intelligent because multilayer learning approaches are inappropriate for dealing with time-series data due to the higher misclassification rate and inherent computational complexity. To address this, the study proposed the radial basis function network that learns the temporal aspects from spectral data and makes use of additional environmental statistics such as frequency, efficiency, energy, spectrum allocation, distance, and duty-cycle time, which are considered environmental data that may be used to fine-tune sensor performance. The scheme is simulated with real-time parameters, and the results are quite promising in terms of evaluation parameters.

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Spectrum Sensing and Resource Allocation for Proficient Transmission in Cognitive Radio with 5G

Cited by 15 publications

References 32 publications

Efficient Centralized Cooperative Spectrum Sensing Techniques for Cognitive Networks

Efficient Centralized Cooperative Spectrum Sensing Techniques for Cognitive Networks

A Cognitive Knowledged Energy-Efficient Path Selection Using Centroid and Ant-Colony Optimized Hybrid Protocol for WSN-Assisted IoT

Spectrum sensing-focused cognitive radio network for 5G revolution

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