A Comparative Study to Classify and Predict the Throughput of Fifth Generation Wireless Technology Using Supervised Machine Learning Algorithms

Sharma, Abhilasha; Pandit, Shweta; Talluri, Salman Raju

doi:10.1109/iciip53038.2021.9702678

Cited by 1 publication

(2 citation statements)

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“…8, 9 depict the comparison of selected state-of-theart throughput classification approaches [113], [114], [115] while the previously presented evaluation analysis is included as well. For each of the [113], [114], [115] works, we pick the best performing ML algorithm, and so we do for our evaluation approach, as far as the 3-class throughput prediction problem is concerned (i.e., k-NN algorithm, see Table 3). As it is apparent, our evaluation approach is consistent with similar approaches in other recent works [113], [114], [115].…”

Section: Simulations and Comparisonmentioning

confidence: 99%

“…For each of the [113], [114], [115] works, we pick the best performing ML algorithm, and so we do for our evaluation approach, as far as the 3-class throughput prediction problem is concerned (i.e., k-NN algorithm, see Table 3). As it is apparent, our evaluation approach is consistent with similar approaches in other recent works [113], [114], [115].…”

Section: Simulations and Comparisonmentioning

confidence: 99%

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ML-Based Radio Resource Management in 5G and Beyond Networks: A Survey

et al. 2022

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In this survey, a comprehensive study is provided, regarding the use of machine learning (ML) algorithms for effective resource management in fifth-generation and beyond (5G/B5G) wireless cellular networks. The ever-increasing user requirements, their diverse nature in terms of performance metrics and the use of various novel technologies, such as millimeter wave transmission, massive multipleinput-multiple-output configurations and non-orthogonal multiple access, render the multi-constraint nature of the radio resource management (RRM) problem. In this context, ML and mobile edge computing (MEC) constitute a promising framework to provide improved quality of service (QoS) for end users, since they can relax the RMM-associated computational burden. In our work, a state-of-the-art analysis of ML-based RRM algorithms, categorized in terms of learning type and potential applications as well as MEC implementations,is presented, to define the best-performing solutions for various RRM sub-problems. To demonstrate the capabilities and efficiency of ML-based algorithms in RRM, we apply and compare different ML approaches for throughput prediction, as an indicative RRM task. We investigate the problem, either as a classification or as a regression one, using the corresponding metrics in each occasion. Finally, open issues, challenges and limitations concerning AI/ML approaches in RRM for 5G and B5G networks, are discussed in detail.

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