Welcome to Journal of Network Communications and Emerging Technologies (JNCET)

Volume 12, Issue 2, February (2022)                               Cover Page and Table of Contents

S.No	Title & Authors	Full Text
1	A Survey on the Wireless Network Technology Classification Using Machine Learning Siddhartha Subray, Kevin Gifford Abstract - With the rapid development of numerous wireless network technologies and the growing number of wireless devices in use around the world, gaining access to the radio frequency spectrum has become a challenge that must be solved as soon as possible. The ever-increasing wireless traffic and shortage of accessible spectrum necessitate smart spectrum management. Machine learning (ML) is gaining popularity, and its capacity to spot patterns and aid decision-making has found applications in a variety of disciplines. Machine learning approaches have been applied to wireless networking difficulties, such as spectrum efficiency, and have showed superior performance compared to traditional methods. Spectrum sensing enables dynamic spectrum sharing, which improves spectrum efficiency by allowing coexistence of wireless technologies within the same frequency range. This involves the accurate detection and identification of multiple wireless signals sent in the same radio spectrum range. The current state of machine learning algorithms for identifying and classifying radio signals depending on their access technologies, such as Wi-Fi and LTE, is examined in this work. Classifying the RF signals based on their wireless network technologies as opposed to their modulation schemes, especially using machine learning, is an emerging area of study and is becoming a popular research topic. This survey will assist readers to become familiar with the current literature and enable further research in this domain.	Download