Dynamic resource allocation in OFDM systems using DE and FRBS

Rahman, Atta; Qureshi, Ijaz Mansoor; Malik, Arshad Nawaz; Naseem, Muhammad Tahir

doi:10.3233/ifs-130880

Cited by 25 publications

(5 citation statements)

References 26 publications

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“…Within the scope of this research, based on radial basis functions, we examine the essential components of superconducting neural networks (RBF) (Schegolev et al, 2022). Qureshi et al (2014) presented a multiple relay selection scheme for underlay CRN in CSI. In this regard, they utilized FRBs and swarm intelligence to reduce the transmit power.…”

Section: Literature Reviewmentioning

confidence: 99%

Spectrum sensing-focused cognitive radio network for 5G revolution

Ali

2023

Front. Environ. Sci.

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The cognitive radio network (CRN), an instrumental part of the next-generation wireless communication systems, is mainly dependent on spectrum sensing to function properly. The radio spectrum can help in clean energy transition and load capacity factors by providing a more efficient and accurate spectrum utilization. By using it, the number of spectrums that is used for communication can be optimized, which can reduce the amount of energy consumed by the network. Additionally, 5G radio spectrum sensing can be used to identify and classify different types of signals, which can help reduce the amount of interference in the network and improve the efficiency of energy utilization. It can also allow for the digitization of clean energy infrastructure and facilitate better decision-making processes that take into account environmental impacts while optimizing energy use because of its efficient characteristics like non-linearity, detection, scalability, robustness, generalization, non-stationarity in wireless environments, dynamic entity, weighted sum of Gaussian functions centered at specific frequencies, and robustness against noise and interference. It can adapt to noise and interference by adjusting its parameters, and this allows accurate distinguishing between primary and secondary users in the wireless spectrum, which is why a radial basis function is a popular choice for spectrum sensing in 5G networks. Radial basis function networks (RBFNs) can work better in 5G spectrum sensing for better signal classification, dynamic adaptation, fast detection, faster decision-making, and improved noise and interference reduction. One of the most sought-after goals in the field of wireless research is the creation of spectrum-sensing technology that is dependable and intelligent because multilayer learning approaches are inappropriate for dealing with time-series data due to the higher misclassification rate and inherent computational complexity. To address this, the study proposed the radial basis function network that learns the temporal aspects from spectral data and makes use of additional environmental statistics such as frequency, efficiency, energy, spectrum allocation, distance, and duty-cycle time, which are considered environmental data that may be used to fine-tune sensor performance. The scheme is simulated with real-time parameters, and the results are quite promising in terms of evaluation parameters.

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Section: Literature Reviewmentioning

confidence: 99%

Spectrum sensing-focused cognitive radio network for 5G revolution

Ali

2023

Front. Environ. Sci.

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“…The dataset, collected on a proxy server, consisted of user machine, network, and web usage logs. The approach combined a fuzzy rule-based system (FRBS) [15][16][17][18][19][20] and Gaussian Radial basis Function Neural Network (GRBF-NN) [21][22][23][24][25] in the neurofuzzy model, which showed promising prediction and classification accuracy.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Approach to Users’ Age Prediction: A Telecom Company Case Study in Saudi Arabia

Gollapalli,

Rahman,

Youldash

et al. 2023

MMEP

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The accurate prediction of demographic attributes of users, including age and gender, is a pivotal challenge in personalized search, ad targeting, and other related fields. This information enables companies to refine their target audience and enhance the overall user experience and quality of service (QoS). Among these, the Saudi Telecommunication Company (STC), a premier telecommunications provider in Saudi Arabia, Middle East, and Africa, recognizes the substantial role of age-prediction systems. This study, therefore, explores the application of machine learning (ML) techniques to predict user age, thus assisting in the delivery of age-appropriate ads and offers. We utilized a dataset provided by STC, comprising three million samples with key user and device features. Four ML algorithms were employed in this analysis: the Artificial Neural Network (ANN), Random Forest (RF), Gradient Boosting (GB), and Decision Tree (DT). These models were compared and evaluated based on their predictive performance. The ANN emerged as the optimal classifier, achieving an accuracy of 60%, comparable to similar studies conducted within the telecommunications industry. The implications of these findings suggest that ML techniques can effectively predict user information, thereby enabling service providers to tailor their offerings to the specific age demographics of their users. The findings from this study contribute to the broader understanding of user age prediction and its practical implications for telecommunications companies. Future research could extend this work by exploring other demographic prediction challenges and applying the ML approach to other sectors.

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“…As there are four edges while in triangular function, there are three edges as investigated in Refs. [25,26].…”

Section: Fuzzy Inference System Membership Functionmentioning

confidence: 99%

Early Identification of COVID-19 Using Dynamic Fuzzy Rule Based System

Ahmed¹,

Rahman²,

Farooqui³

et al. 2021

MMEP

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The undergoing research aims to address the problem of COVID-19 which has turned out to be a global pandemic. Despite developing some successful vaccines, the pace has not overcome so far. Several studies have been proposed in the literature in this regard, the present study is unique in terms of its dynamic nature to adapt the rules by reconfigurable fuzzy membership function. Based on patient’s symptoms (fever, dry cough etc.) and history related to travelling, diseases/medications and interactions with confirmed patients, the proposed dynamic fuzzy rule-based system (FRBS) identifies the presence/absence of the disease. This can greatly help the healthcare professionals as well as laymen in terms of disease identification. The main motivation of this paper is to reduce the pressure on the health services due to frequent test assessment requests, in which patients can do the test anytime without the need to make reservations. The main findings are that there is a relationship between the disease and the symptoms in which some symptoms can indicate the probability of the presence of the disease such as high difficulty of breathing, cough, sore throat, and so many more. By knowing the common symptoms, we developed membership functions for these symptoms, and a model generated to distinguish between infected and non-infected people with the help of survey data collected. The model gave an accuracy of 88.78%, precision of 72.22%, sensitivity of 68.42%, specificity of 93.67%, and an f1-score of 69.28%.

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Dynamic resource allocation in OFDM systems using DE and FRBS

Cited by 25 publications

References 26 publications

Spectrum sensing-focused cognitive radio network for 5G revolution

Spectrum sensing-focused cognitive radio network for 5G revolution

Machine Learning Approach to Users’ Age Prediction: A Telecom Company Case Study in Saudi Arabia

Early Identification of COVID-19 Using Dynamic Fuzzy Rule Based System

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