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Machine learning to predict the quality of a wireless channel


Reference persons STEFANO SCANZIO

Research Groups CNR-IEIIT, comunicazioni industriali

Thesis type RESEARCH

Description This thesis proposal aims to explore the application of machine learning techniques to predict the future quality of wireless channels. Wireless networks play a vital role in our daily lives, providing connectivity for various devices and services. However, the quality of wireless channels can fluctuate due to various factors, such as interference, signal fading, and environmental conditions. Being able to anticipate channel quality changes in advance can greatly enhance the performance and reliability of wireless communication systems.

The objective of this research is to develop a predictive model that utilizes machine learning algorithms to forecast the future quality of wireless channels. By analyzing historical data collected from wireless networks, the model will learn patterns and relationships between different parameters and channel quality variations. Various features, such as signal strength, noise level, bandwidth utilization, and network traffic patterns, will be considered as inputs to the model.

By successfully implementing this research, we aim to provide wireless network operators and system designers with a valuable tool for proactive network management and optimization. The ability to predict future channel quality can enable intelligent resource allocation, adaptive modulation schemes, and proactive measures to mitigate potential degradation in wireless communication systems. For more information or ask for more theses please contact me: https://www.skenz.it/ss

See also  https://youtu.be/t5ZuavBPWnA

Required skills Good knowledge of python and keras

Deadline 08/06/2025      PROPONI LA TUA CANDIDATURA