Clustering formation in cognitive radio networks using machine learning

Bhatti, Dost Muhammad Saqib; Ahmed, Saleem; Chan, A. Sattar; Saleem, Kashif

doi:10.1016/j.aeue.2019.152994

Cited by 19 publications

(10 citation statements)

References 13 publications

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“…8 Bhatti et al proposed a novel method which segregates the network into clusters to improve the performance of cooperative spectrum sensing. 9 They used artificial intelligence to make the clusters that are formed based on machine learning affinity propagation algorithm. Zhu et al proposed a machine-learning-based opportunistic spectrum access framework by integrating multiarmed bandit and matching theory.…”

Section: Related Workmentioning

confidence: 99%

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Employing machine learning based malicious signal detection for cognitive radio networks

TÜRKYILMAZ

Şentürk

Bayrakdar

2022

Concurrency and Computation

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In cognitive radio networks, the empty spectrum that is also named as spectrum hole is detected with the help of spectrum sensing techniques. Energy detection is the most utilized spectrum sensing technique owing to its low complexity. In the energy detection technique, a spectrum hole is detected with a predefined threshold. In this article, machine learning based malicious signal detection is employed for cognitive radio networks. The design of cognitive radio users and network environment is simulated with Riverbed simulation software. The received signal is controlled whether it is a malicious signal or just a secure sensing signal. The fuzzy logic based system is utilized for the security categorization of spectrum sensing signals as malicious, suspicious, and secure sensing signals. Fuzzy logic parameters are taken from the machine learning features that are chosen as the most effective 3 features among all 49 features. The security of primary users is enhanced when compared to other schemes in the literature. The results of the proposed machine learning based malicious signal detection system are validated with the results acquired from the fuzzy logic based approach.The random forest method gives the best results among all machine learning methods for the detection of signals.

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Section: Related Workmentioning

confidence: 99%

“…Bhatti et al proposed a novel method which segregates the network into clusters to improve the performance of cooperative spectrum sensing 9 . They used artificial intelligence to make the clusters that are formed based on machine learning affinity propagation algorithm.…”

Section: Related Workmentioning

confidence: 99%

Employing machine learning based malicious signal detection for cognitive radio networks

TÜRKYILMAZ

Şentürk

Bayrakdar

2022

Concurrency and Computation

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“…The study in [16] addressed the overhead problem associated with many SUs which send their observation to the fusion center by partitioning a network into clusters. Clusters send their observation to the fusion center through cluster heads.…”

Section: Related Workmentioning

confidence: 99%

Investigative analysis of channel selection algorithms in cooperative spectrum sensing in cognitive radio networks

Tlouyamma

Velempini

2021

SAIEE Afr. Res. J.

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The proliferation of wireless mobile devices has led to a number of challenges in mobile data communication. The world is experiencing an increasing usage of finite spectrum bands for social media and other data communication services. It is due to this high usage that the Federal Communications Commission (FCC) sought to open up some spectrum bands to be used opportunistically by secondary users (SUs). However, the coexistence of Primary Users (PUs) and SUs may cause interference which leads to wastage of spectrum resources. This study investigates the impact of interferences between PUs and SUs. To ensure higher detection of PU signal, a cooperative rule was used to decide which SU to share and make a final decision about the availability of the spectrum band. To maximize the throughput of SU, a maximum likelihood function was designed to reduce delays in searching for the next available channel for data transmission. To discover more transmission opportunities and ensuring that a good number of free channels are detected, a parallel sensing technique was employed. Matlab was used to simulate and generate the results in a distributed cognitive radio environment. The proposed extended generalized predictive channel selection algorithm (EXGPCSA) outperformed other schemes in literature in terms of throughput, service time and probability of detection.

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“…[22]. The ML procedures and their efficiency for various communication fields such as software-defined networks [23], Internet of Things [24], wireless sensor networks [25], cognitive radios [26], wireless networks [27], computer networks [28] and traffic classification [29] have been studied.…”

Section: Introductionmentioning

confidence: 99%

Supervised regression modelling for mitigation of four-wave mixing in dense wavelength-division multiplexing systems

Venkatesan¹,

Chandrasekar²,

Ramesh³

2021

Ukr. J. Phys. Opt.

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A recent global crisis associated with COVID-19 has encouraged millions of people to work from home, thus causing a drastic increase in overall network traffic, data-rate requirements and end network capabilities. This has also produced more noise, cross-talk and undesirable optical-fibre nonlinearities, especially a fourwave mixing (FWM) effect that deteriorates performance of dense wavelengthdivision multiplexing (DWDM) systems. A presence of FWM in the DWDM systems imposes increasing complexity and latency of networks, and decreases their spectral efficiency. In its turn, this degrades efficient utilization of optical bandwidth. To mitigate the above problems, we suggest a supervised regression modelling (SRM). A relevant SRM-DWDM approach performs self-parametric optimization of the DWDM systems with machine-learning techniques and finds real trade-offs among various factors that affect the FWM. Our model reduces complexity of modelling and computational time, resulting in accurate and reliable prediction of parameter values. We also evaluate the performance of our SRM-DWDM technique by comparing its data with the iterative results obtained for different parameters (e.g., output signal-to-noise ratio, Q-factor, signal power and noise power). Finally, we specify the procedures necessary for global optimization of DWDM systems.

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Clustering formation in cognitive radio networks using machine learning

Cited by 19 publications

References 13 publications

Employing machine learning based malicious signal detection for cognitive radio networks

Employing machine learning based malicious signal detection for cognitive radio networks

Investigative analysis of channel selection algorithms in cooperative spectrum sensing in cognitive radio networks

Supervised regression modelling for mitigation of four-wave mixing in dense wavelength-division multiplexing systems

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