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Your search keyword '"Uma Mageswari, R."' showing total 11 results
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11 results on '"Uma Mageswari, R."'

7. Establishment of FANETs Using IoT-Based UAV and Its Issues Related to Mobility and Authentication

Author

Uma Mageswari R., Nallarasu Krishnan, Mohammed Sirajudeen Yoosuf, Murugan K., and Sankar Ram C.

Abstract

The tremendous evolution of wireless communication as well as the drastic adoption of technology by the latest computing devices known to be IoT, makes it possible for emerging applications to providing ubiquitous services. This technique transformed the quality of present lifestyle of the people. When compared with all other technologies, the mobile adhoc networks become widely adapted in many fields because of the non-requirement of centralized infrastructure support. Adopting this nature, it became easy to establish networks like WSN and also form networks using IoT devices. As FANET (flying/fast adhoc network) is known for its mobility and instant formation of network with the help of available nodes within its communication range, there is a great challenge related to mobility and authenticity of the participating devices by exempting malicious nodes. FANETs incorporate unmanned aerial vehicles and drones as a part of their communication networks. In this chapter, deployment of IoT-based FANETs along with mobility and security is handled.

Published
2022

11. Machine Learning Empowered Accurate CSI Prediction for Large-Scale 5G Networks.

Author

Uma Mageswari, R., Gousebaigmohammad, Dulam, Devee siva prasad, Shitharth, S., Surya Narayana, G., Suresh, A., JaikumarR, Bojaraj, Leena, Chandragandhi, S., and GosuAdigo, Amsalu

Subjects
MULTIPATH channels, CHANNEL estimation, BIT error rate, 5G networks, SUPERVISED learning, MACHINE learning, TELECOMMUNICATION systems
Abstract

Wi-Fi networks rely on channel estimation to ensure their performance. The computational complexity and dependability of fifth generation telecommunication networks have significantly improved using supervised learning. In this paper, we develop a channel estimation model that uses a machine learning approach and the study uses multipath channel simulations for the estimation of channel state information (CSI) over arbitrary transceiver antennas. The simulation is conducted to test the efficacy of the model against various machine learning channel estimation models. The results of simulation show that the proposed model obtains increased channel estimation quality than other methods. Further, the bit error rate is recorded low among other methods using the machine learning model. Thus, it is seen that the proposed method achieves a reduced mismatch rate of 1.26 × 10 − 1.5 than other methodson Doppler frequency during channel estimation, where the mismatch rate is higher in existing methods. [ABSTRACT FROM AUTHOR]

Published
2022
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