Performance Comparison of Machine Learning Algorithms in Symbol Detection Using OFDM

Seeram, Siva Satya Sri Ganesh; Reddy, Avuthu Yuvaraja; Basil, Noorulden; Suman, Akella Viswa Sai; Anuraj, K; Poorna, S. S.

doi:10.1007/978-981-16-5529-6_36

Cited by 1 publication

(1 citation statement)

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“…Among the most recent papers, there are [70], in which a Machine Learning solution for solving the line-of-sight discovery problem in indoor mmWave Wi-Fi networks is proposed. Another example is [71], where the authors compare various Machine Learning algorithms to detect symbols in orthogonal frequencydivision multiplexing transmissions. The three most representative documents for this topic are [72][73][74].…”

Section: Topic Modeling Resultsmentioning

confidence: 99%

A Systematic Review of Wi-Fi and Machine Learning Integration with Topic Modeling Techniques

Atzeni

Bacciu

Mazzei

et al. 2022

Sensors

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Wireless networks have drastically influenced our lifestyle, changing our workplaces and society. Among the variety of wireless technology, Wi-Fi surely plays a leading role, especially in local area networks. The spread of mobiles and tablets, and more recently, the advent of Internet of Things, have resulted in a multitude of Wi-Fi-enabled devices continuously sending data to the Internet and between each other. At the same time, Machine Learning has proven to be one of the most effective and versatile tools for the analysis of fast streaming data. This systematic review aims at studying the interaction between these technologies and how it has developed throughout their lifetimes. We used Scopus, Web of Science, and IEEE Xplore databases to retrieve paper abstracts and leveraged a topic modeling technique, namely, BERTopic, to analyze the resulting document corpus. After these steps, we inspected the obtained clusters and computed statistics to characterize and interpret the topics they refer to. Our results include both the applications of Wi-Fi sensing and the variety of Machine Learning algorithms used to tackle them. We also report how the Wi-Fi advances have affected sensing applications and the choice of the most suitable Machine Learning models.

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Section: Topic Modeling Resultsmentioning

confidence: 99%