Paper Contents
Abstract
Wireless communication is undergoing a paradigm shift with emergence of high performance machine learning (ML) computing and internet of things (IoT). The demand for bandwidth has significantly risen due to multimedia applications and high speed data transfer. However, with increasing number of cellular users, the challenge is to effectively manage the limited spectrum allotment for wireless communication while maintaining satisfactory quality of service. Hence, different multiplexing techniques have been used to effectively use the available bandwidth. Recently, the concept of automatic fallback in receivers are gaining popularity due to high mobility in vehicular networks and IoT. Automatic fallback and handover mechanisms often utilize the channel state information (CSI) of the radio and can switch between technologies to provide the best available quality of service for particular spatial and temporal channel conditions. With the advent of machine learning and deep learning methods, estimating the channel state information has become computationally efficient and feasible thereby improving the performance metrics of the system. This paper presents a comprehensive review on the need for cognitive systems with CSI availability, handover mechanisms in wireless networks and different strategies involved in estimating the channel state information for wireless networks.
Copyright
Copyright © 2023 mukul jain . This is an open access article distributed under the Creative Commons Attribution License.
