Episode 2: Guest Lecture: Importance sampling in wireless communication

In the ever-evolving field of wireless communication, accurately estimating the performance of digital communication systems is crucial. One of the most effective tools for this task is Monte Carlo (MC) simulation, a technique that has been widely used for over 70 years. However, MC simulations can be computationally intensive, especially when dealing with rare events or low-probability occurrences that significantly impact system performance.
This lecture delves into the advanced technique of importance sampling (IS), a powerful method designed to enhance the efficiency of Monte Carlo simulations. Importance sampling strategically focuses on rare events, reducing the variance of the estimator and achieving reliable results with significantly fewer samples.
Participants will learn:
1. Fundamentals of Monte Carlo Simulations: Understanding the basics of MC simulations and their application in evaluating digital communication systems.
2. Challenges with Rare Events: Exploring the difficulties in directly sampling rare events and the computational burden associated with traditional MC simulations.
3. Introduction to Importance Sampling: A comprehensive overview of IS, including its principles and how it modifies the sampling process to focus on rare events.
4. Performance Enhancement with IS: Demonstrating how IS reduces simulation time and computational power requirements while maintaining high accuracy.
5. Practical Implementations: Evaluating current state-of-the-art IS techniques across different channel models, including additive white Gaussian noise (AWGN) and Rayleigh fading channels.
By the end of this lecture, attendees will gain a deep understanding of how importance sampling can revolutionize simulation efficiency in wireless communication, providing a robust framework for accurately assessing system performance with reduced computational resources.

[@DzevdanKapetanovic]

A minor correction to my comment on the Error Probability slide in my presentation: the indicator function is correct as it is - it should not be a Dirac delta.