Machine Learning-Based Algorithm for Channel Selection Utilizing Preemptive Resume Priority in Cognitive Radio Networks Validated by NS-2

Sumathi, D.; Manivannan, Siyamalan

doi:10.1007/s00034-019-01140-y

Cited by 16 publications

(11 citation statements)

References 15 publications

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“…Statistics models are generally utilized involving Multiple Linear Regression (MLR) (Awang et al, 2015), Principal Component Analysis (PCA) (Wuttichaikitcharoen & Babel, 2014). Nowadays, ML techniques are speedily improved in order to empower the standard environmental predictions performances because of their nonlinear mapping capability, like Support Vector Machine (SVM) (He et al, 2017;Mehdipour & Memarianfard, 2019;Mehdipour et al, 2018;Sumathi & Manivannan, 2020), Extreme Gradient Boosting (XGBoost) (Ma et al, 2020), Bayesian Network (Mehdipour et al, 2018), and Random Forest (Feng et al, 2019). The previously mentioned ML techniques deal with nonlinearity cases and showed the high capability to 69 captures temporal characteristics of renewable resources as well as air pollutants.…”

Section: Time Series Prediction Techniquesmentioning

confidence: 99%

Hybrid deep learning model for ozone concentration prediction: comprehensive evaluation and comparison with various machine and deep learning algorithms

Yafouz

Ahmed

Zaini

et al. 2021

Engineering Applications of Computational Fluid Mechanics

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To accurately predict tropospheric ozone concentration(O 3 ), it is needed to investigate the variety of artificial intelligence techniques' performance, such as machine learning, deep learning and hybrid models. This research aims to effectively predict the hourly ozone trend via fewer input variables. This ozone prediction attempt is performed on diversity data of air pollutants (NO 2 , NO x , CO, SO 2 ) and meteorological parameters (wind-speed and humidity). The historical datasets are collected from 3 sites in Malaysia. The study's methodology progressed in two paths: standalone and hybrid models where hourly-averaged datasets are applied based on 5-time horizon analysis scenario, with different inputs' combinations. For evaluation, all models are tested throughout 5-performance indicator and illustrated on Modified Taylor diagram. Sensitivity analysis of input variables is quantified. Additionally, uncertainty analysis is conducted to assess their confidence level associated with Willmott Index. Based on R 2 , results indicated that XGBoost has higher accuracy compared to MLP and SVR; meanwhile, LSTM and CNN outweighs XGBoost. In terms of robustness and accuracy, the proposed hybrid model possesses superlative performance compared to all above-mentioned techniques. The proposed model achieved exceptional results as the highest R 2 , the highest 95% confidence degree, and narrower confidence interval width, are 93.48%, 98.16%, and 0.0014195, respectively.

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Section: Time Series Prediction Techniquesmentioning

confidence: 99%

Hybrid deep learning model for ozone concentration prediction: comprehensive evaluation and comparison with various machine and deep learning algorithms

Yafouz

Ahmed

Zaini

et al. 2021

Engineering Applications of Computational Fluid Mechanics

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“…Authors [19] designed a novel machine learning algorithm; the support vector machine to select the best possible free channel for transmission by SUs. The algorithm makes use of four parameters to select a better channel.…”

Section: Related Workmentioning

confidence: 99%

“…And its logarithm gives a positive result. Therefore, the decision rule in (19) can further be simplified to:…”

Section: A Proposed Cooperative Spectrum Sensing and Aggregationmentioning

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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“…Using machine learning, the cognitive engine would be able to coordinate the actions of the CR users. Recently, applying machine learning to CRNs has become an interesting research topic [261]- [263]. As an example, learning techniques can be used to estimate wireless channel characteristics and to choose a specific coding rate that results in reduction of possible errors.…”

Section: B2)mentioning

confidence: 99%

Multimedia Communication over Cognitive Radio Networks from QoS/QoE Perspective; A Comprehensive Survey

Piran¹

2020

Preprint

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The stringent requirements of wireless multimedia transmission lead to very high radio spectrum solicitation. Although the radio spectrum is considered as a scarce resource, the issue with spectrum availability is not scarcity, but the inefficient utilization. Unique characteristics of cognitive radio (CR) such as flexibility, adaptability, and interoperability, particularly have contributed to it being the optimum technological candidate to alleviate the issue of spectrum scarcity for multimedia communications. However, multimedia communications over CR networks (MCRNs) as a bandwidth-hungry, delay-sensitive, and loss-tolerant service, exposes several severe challenges specially to guarantee quality of service (QoS) and quality of experience (QoE). As a result, to date, different schemes based on source and channel coding, multicast, and distributed streaming, have been examined to improve the QoS/QoE in MCRNs. In this paper, we survey QoS/QoE provisioning schemes in MCRNs. We first discuss the basic concepts of multimedia communication, CRNs, QoS and QoE. Then, we present the advantages of utilizing CR for multimedia services and outline the stringent QoS and QoE requirements in MCRNs. Next, we classify the critical challenges for QoS/QoE provisioning in MCRNs including spectrum sensing, resource allocation management, network fluctuations management, latency management, and energy consumption management. Then, we survey the corresponding feasible solutions for each challenge highlighting performance issues, strengths, and weaknesses. Furthermore, we discuss several important open research problems and provide some avenues for future research.

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Machine Learning-Based Algorithm for Channel Selection Utilizing Preemptive Resume Priority in Cognitive Radio Networks Validated by NS-2

Cited by 16 publications

References 15 publications

Hybrid deep learning model for ozone concentration prediction: comprehensive evaluation and comparison with various machine and deep learning algorithms

Hybrid deep learning model for ozone concentration prediction: comprehensive evaluation and comparison with various machine and deep learning algorithms

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

Multimedia Communication over Cognitive Radio Networks from QoS/QoE Perspective; A Comprehensive Survey

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