Spectral efficiency enhancement of green metric cognitive radio network using novel channel design and intellectual African buffalo optimization

Kumar, Maddali Arun; Siddaiah, P.

doi:10.1007/s12652-021-03159-z

Cited by 7 publications

(2 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Te incorporation of collaborative communication technology makes cognitive wireless networks more academically signifcant and valuable. In a collaborative cognitive radio network (CCRN), authorized and unauthorized users are able to share the same frequency band, i.e., unauthorized users gain access and use spectrum by assisting authorized users to achieve spectrum sharing during forwarding data [8]. For collaborative relay user selection, it is an important issue to choose the appropriate relay node to collaborate with authorized users because of the diferences in geographic location and channel fading parameters of each relay node and data transmission capabilities [9].…”

Section: Introductionmentioning

confidence: 99%

Collaborative Cognitive Wireless Network Optimization Model and Network Parameter Optimization Algorithm

Zhang¹

2023

Journal of Electrical and Computer Engineering

View full text Add to dashboard Cite

In recent years, the combination of cognitive radio and collaborative communication has been widely studied and applied because of its ability to increase user throughput and improve spectrum utilization in a flat-fading wireless channel environment. Such cognitive radio networks that use user collaboration to improve channel capacity and spectrum utilization are called collaborative cognitive radio networks. A Nash equilibrium game-based relay node selection algorithm is investigated, which aims to maximize the utility function of primary and cognitive users. Secondly, a Stackelberg game is introduced, which aims to select the better set of nodes to achieve spectrum sharing. Simulation results show that the algorithm proposed in the study maximizes the utility functions of both primary and cognitive users and enables the selection of a better set of nodes for spectrum sharing. Specifically, the Nash equilibrium game-based relay node selection algorithm at c = 0.3 ∗ 10−6 results in better utility values for both PU and CU, and the algorithm enables more CU to access the spectrum so that users can get longer access time. The relay node selection algorithm based on the Stackelberg game demonstrates high feasibility. Under the condition of parameter α = α ∗ , the algorithm can achieve high-quality cooperation, and CU in better positions can be used as relay cooperation nodes. The algorithm can improve the main user utility function by 20%–35%.

show abstract

Section: Introductionmentioning

confidence: 99%

Collaborative Cognitive Wireless Network Optimization Model and Network Parameter Optimization Algorithm

Zhang¹

2023

Journal of Electrical and Computer Engineering

View full text Add to dashboard Cite

show abstract

“…A cognitive radio network (CRN) is an emerging technology for solving spectrum scarcity issues by sensing and utilizing unoccupied spectrums or spectrum holes, which also enhances spectral efficiency in the CRN [ 1 ]. Next-generation communication technologies such as 5G and 6G provide the connection for the Internet of Connected Vehicles (IoCV) (i.e., autonomous vehicles, self-driving cars) [ 2 , 3 ].…”

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

View full text Add to dashboard Cite

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.

show abstract

Channel estimation in 5G multi input multi output wireless communication using optimized deep neural framework

Kapula¹,

Sridevi

2022

Cluster Comput

View full text Add to dashboard Cite

Spectral efficiency enhancement of green metric cognitive radio network using novel channel design and intellectual African buffalo optimization

Cited by 7 publications

References 29 publications

Collaborative Cognitive Wireless Network Optimization Model and Network Parameter Optimization Algorithm

Collaborative Cognitive Wireless Network Optimization Model and Network Parameter Optimization Algorithm

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

Channel estimation in 5G multi input multi output wireless communication using optimized deep neural framework

Contact Info

Product

Resources

About