Primary User Traffic Pattern Based Opportunistic Spectrum Handoff in Cognitive Radio Networks

Arshid, Kaleem; Hussain, Iftikhar; Bashir, Muhammad Khawar; Naseem, Shahid; Ditta, Allah; Mian, Natash Ali; Zahid, Misha; Khan, Israr Ali

doi:10.3390/app10051674

Cited by 19 publications

(7 citation statements)

References 55 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…x ✓ Centralized cognitive network/distributed cognitive network/cluster-based network 54,57,85 Abbreviation: IWC, industrial wireless communications.…”

Section: Suitability Of Different Pu Signal Detection Techniques For mentioning

confidence: 99%

See 1 more Smart Citation

A review of industrial wireless communications, challenges, and solutions: A cognitive radio approach

Oyewobi

Djouani

Kurien

2020

Trans Emerging Tel Tech

View full text Add to dashboard Cite

Integral and crucial to performance of wireless sensor networks (WSNs), and specifically industrial wireless sensor network (IWSN) is stable, robust, reliable, and ubiquitous communications system. Though, wired communications system is suitable for industrial communications and is resilient to shadowing and multipath fading effects of industrial‐WSN environments, yet its wireless counterpart is a much preferred industrial communications technology due to reduced cost and high flexibility which it offers in comparison to wired communications. However, overcrowding of the industrial, scientific, and medical band, where IWSN is deployed together with other heterogeneous technologies, as well as resultant scarcity of usable frequency spectrum has restrained exclusive application of wireless technology for industrial communications. Nonetheless, cognitive radio (CR) has ability to increase spectrum utilization efficiency and channel capacity for industrial wireless communications (IWC) through opportunistic/dynamic spectrum access (DSA) technique. In this review article, we examine how DSA can benefit IWC through exploitation of new perspectives of white space definitions in the licensed bands as well as unlicensed bands. While discussing the potential of DSA for IWC, we have considered the unique characteristics of IWC as well as technical challenges and issues imposed by industrial‐WSN. Accordingly, we have suggested and proffered appropriate CR‐based solutions in mitigating some of the challenges where necessary.

show abstract

“…x ✓ Centralized cognitive network/distributed cognitive network/cluster-based network 54,57,85 Abbreviation: IWC, industrial wireless communications.…”

Section: Suitability Of Different Pu Signal Detection Techniques For mentioning

confidence: 99%

“…TA B L E 3 Summary of different spectrum handoff strategies 85,105,[107][108][109][111][112][113][114]…”

Section: Comparison Of Different Spectrum Handoff Strategiesmentioning

confidence: 99%

A review of industrial wireless communications, challenges, and solutions: A cognitive radio approach

Oyewobi

Djouani

Kurien

2020

Trans Emerging Tel Tech

View full text Add to dashboard Cite

show abstract

“…However, it can also cause poor handoff due to the delays in the tra c [15,16]. The process of PUTPOSH is to determine if a handoff is required based on the arrival of the PU and the service time [17]. The queue model used in this process is called the M/G/1 queue.…”

Section: Introductionmentioning

confidence: 99%

Experimental Evaluation of Spectrum Handoff Management with Machine Learning Algorithms using Software Defined Radio

Babjan

Rajendran

2023

Preprint

View full text Add to dashboard Cite

Although the design of spectrum switching has been studied, little is known about how random user movement affects the handoff. This issue can occur when a user moves to a new location. In this paper, the authors present a framework that verifies the necessity of spectrum handoff to improve the performance of the system by implementing machine learning (ML) techniques. Some of these include the Logistic Regression, KNN Algorithm, SVM Algorithm, Naïve Bayes Classifier, Decision Tree Classification and Random Forest Algorithm. The system is implemented on a real-time dataset where all the users are separated in power domain using the concept of non orthogonal multiple access (NOMA) technique. The dataset values are prepared using a software-defined radio experimental setup, which is used to analyze the performance of various ML techniques in terms of confusion matrix, specificity, precision, F1_score, sensitivity and accuracy.

show abstract

“…In this case, the sensing spectrum is dynamically assigned to the SUs while PU inactivity leads to spectrum efficiency [ 10 ]. In dynamic spectrum sensing, the sensed spectrum is handed off by selecting a target channel, which is known as the mobility management of the spectrum [ 11 , 12 ]. Spectrum handoff includes three conditions: When the SU connection is failing due to mobility; When the sensed spectrum does not satisfy the requirements; When the inactivity of the PU is detected.…”

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

Primary User Traffic Pattern Based Opportunistic Spectrum Handoff in Cognitive Radio Networks

Cited by 19 publications

References 55 publications

A review of industrial wireless communications, challenges, and solutions: A cognitive radio approach

A review of industrial wireless communications, challenges, and solutions: A cognitive radio approach

Experimental Evaluation of Spectrum Handoff Management with Machine Learning Algorithms using Software Defined Radio

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

Contact Info

Product

Resources

About