Channel modelling simulation and machine learning

Abstract

In the ever-evolving landscape of modern communication systems, the ability to understand and optimise channel behaviour is crucial for achieving reliable and efficient data transmission. Channel modelling is pivotal in this endeavour by providing insights into how signals propagate through diverse media and environments. While conventional channel modelling techniques have been effective, they often fall short of capturing the intricacies of real-world scenarios.

This report delves into the intersection of channel modelling, simulation, and machine learning, offering a comprehensive overview of the synergies and advancements in these fields. A standard approach to conducting this experiment would be to gather a vast quantity of data on channel measurements before applying statistical techniques to choose the best channel models [1].

The incorporation of simulation and machine learning methodologies into channel modelling is a revolutionary strategy that enables us to address intricate communication problems with increased accuracy and flexibility.

In this report, we explore the fundamental concepts of channel modelling and its significance in communication systems. We then delve into the realm of simulation, discussing how it aids in creating realistic environments for channel testing and validation. Subsequently, aiming to incorporate machine learning techniques to optimised and automated the communication systems based on data-driven insights.