A Survey on Energy Aware Cognitive Radio Network

Jaronde, Pravin W.; Vyas, Archana; Gaikwad, Mahendra

doi:10.1109/parc52418.2022.9726654

Cited by 11 publications

(4 citation statements)

References 35 publications

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“…This dynamic access consists of three main models such as Dynamic exclusive model, hierarchical access model, and open sharing model. In [32], authors investigated the routing performance of network characteristics called Cognitive Radio Q-routing, in which the Reinforcement Learning (RL) approach was applied. The considerations of unpredictability and dynamicity and finding the lease cost destination routes were determined using CRQ routing.…”

Section: Literature Surveymentioning

confidence: 99%

Analysis of Secure Cognitive Radio Network with Optimal Resource Management

Kumar,

Sharanya

2023

ijmst

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During the last decade, Cognitive Radio Network (CRN) technology has been a significant advance in addressing the ever-increasing spectrum demand. As the number of licensed and unlicensed users in a network rises to complete a certain activity, the information exchange between various types of traffic becomes more complex and difficult. Congestion in CRN is also caused by the conflict among several users for channel access (PUs and SUs). In a very crowded network, many applications perform badly owing to packet collisions and, as a result, packet loss before significant buffer queue building. This circumstance is aggravated by an increase in network users. Congestion control is a vital and essential aspect of the present research issue in communication networks. Several recent reviews in the literature indicate that the congestion issue in the CRN has not been thoroughly studied. Thus, effective and efficient congestion control strategies are sought to optimize network resource usage and management. To prevent congestion, it is crucial for CRN to do research on the creation of an efficient congestion management system. This will improve the network's resource consumption and performance. "Performance improvement via efficient spectrum management through optimum resource management and congestion control in the CRN by mitigating different threats" is the primary target of this project. This research also focused on enhancing performance by addressing security issues in an IoT-based CRN environment. This study provides a comprehensive review of several similar studies and their limitations, which may be used to formulate a new research target.

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Section: Literature Surveymentioning

confidence: 99%

Analysis of Secure Cognitive Radio Network with Optimal Resource Management

Kumar,

Sharanya

2023

ijmst

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“…On the other hand, Recommendation System (RS) offers an exciting solution to resolve the difficulties present in an e-commerce website. It makes use of the nature and details of the consumer, and product details to find the consumer interests and periodically recommend the products which can buy [1]. Several methods have been presented to design RS, and various practical systems have been profitably realized in diverse business environments.…”

Section: Introductionmentioning

confidence: 99%

Recommendation Model-Based 5G Network and Cognitive System of Cloud Data with AI Technique in IOMT Applications

Vekariya¹,

Kannan²,

Gupta³

et al. 2022

Int. j. commun. netw. inf. secur.

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Recommender system provides the significant suggestion towards the effective service offers for the vast range of big data. The Internet of Things (IoT) environment exhibits the value added application services to the customer with the provision of the effective collection and processing of information. In the extension of the IoT, Internet of Medical Things (IoMT) is evolved for the patient healthcare monitoring and processing. The data collected from the IoMT are stored and processed with the cognitive system for the data transmission between the users. However, in the conventional system subjected to challenges of processing big data while transmission with the cognitive radio network. In this paper, developed a effective cognitive 5G communication model with the recommender model for the IoMT big data processing. The proposed model is termed as Ranking Strategy Internet of Medical Things (RSIoMT). The proposed RSIoMT model uses the distance vector estimation between the feature variables with the ranking. The proposed RSIoMT model perform the recommender model with the ranking those are matches with the communication devices for improved wireless communication quality. The proposed system recommender model uses the estimation of direct communication link between the IoMT variables in the cognitive radio system. The proposed RSIoMT model evaluates the collected IoMT model data with the consideration of the four different healthcare datasets for the data transmission through cognitive radio network. Through the developed model the performance of the system is evaluated based on the deep learning model with the consideration of the collaborative features. The simulation analysis is comparatively examined based on the consideration of the wireless performance. Simulation analysis expressed that the proposed RSIoMT model exhibits the superior performance than the conventional classifier. The comparative analysis expressed that the proposed mode exhibits ~3 – 4% performance improvement over the conventional classifiers. The accuracy of the developed model achieves 99% which is ~3 – 9% higher than the conventional classifier. In terms of the channel performance, the proposed RSIoMT model exhibits the reduced recommender relay selection count of 1 while the other technique achieves the relay value of 13 which implies that proposed model performance is ~4-6% higher than the other techniques.

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“…Cognitive radio innovation is one of the procedures to deal with the proficient utilization of the accessible spectrum. In cognitive radio networks, unlicensed clients can utilize the authorized band spectrum when it is free [1]. The pernicious client (MU) is disturbing the spectrum sensing system which goes after the essential sign discovery and influences the precision of the sensing result [2].…”

Section: Introductionmentioning

confidence: 99%

Energy Aware Channel Allocation with Spectrum Sensing in Pilot Contamination Analysis for Cognitive Radio Networks

Joshi¹,

Pandey²,

Aarju³

et al. 2022

Int. j. commun. netw. inf. secur.

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Cognitive radio (CR) is an innovative and contemporary technology that has been making an effort to overcome the problems of bandwidth reduction by rising the usage of mobile cellular bandwidth connections. The reallocation and distribution of channels is a fundamental characteristic of cellular mobile networks (CMN) to exploit the consumption of CMS. Meanwhile, throughput maximization might lead to higher power utilization, the spectrum sensing system must tackle the energy throughput tradeoff. The spectrum sensing time should be defined by the residual battery energy of secondary user (SU). In that context, energy effective algorithm for spectrum sensing should be developed for meeting the energy constraint of CRN. This study designs a new quantum particle swarm optimization-based energy aware spectrum sensing scheme (QPSO-EASSS) for CRNs. Here, the presented QPSO-EASSS technique dynamically estimates the sensing time depending upon the battery energy level of SUs and the transmission power can be computed based on the battery energy level and PU signal of the SUs. In addition, in this work, the QPSO-EASSS technique applies the QPSO algorithm for throughput maximization with energy constraints in the CRN. The detailed set of experimentations take place and reported the improvements of the QPSO-EASSS technique compared to existing models.

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A Survey on Energy Aware Cognitive Radio Network

Cited by 11 publications

References 35 publications

Analysis of Secure Cognitive Radio Network with Optimal Resource Management

Analysis of Secure Cognitive Radio Network with Optimal Resource Management

Recommendation Model-Based 5G Network and Cognitive System of Cloud Data with AI Technique in IOMT Applications

Energy Aware Channel Allocation with Spectrum Sensing in Pilot Contamination Analysis for Cognitive Radio Networks

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