Enabling Simulation-Based Optimization Through Machine Learning: A Case Study on Antenna Design

TL;DR

This paper introduces a machine learning-based approach to emulate complex simulators, enabling rapid global optimization of antenna configurations in mmWave cellular systems with minimal computational resources.

Contribution

The work demonstrates how ML models can reliably emulate complex simulators, significantly reducing optimization time and enabling practical global searches in high-dimensional parameter spaces.

Findings

ML emulators can accurately replicate simulator outputs with limited data

Optimization over large parameter spaces becomes feasible in near-instantaneous time

Method applied successfully to antenna design for mmWave cellular systems

Abstract

Complex phenomena are generally modeled with sophisticated simulators that, depending on their accuracy, can be very demanding in terms of computational resources and simulation time. Their time-consuming nature, together with a typically vast parameter space to be explored, make simulation-based optimization often infeasible. In this work, we present a method that enables the optimization of complex systems through Machine Learning (ML) techniques. We show how well-known learning algorithms are able to reliably emulate a complex simulator with a modest dataset obtained from it. The trained emulator is then able to yield values close to the simulated ones in virtually no time. Therefore, it is possible to perform a global numerical optimization over the vast multi-dimensional parameter space, in a fraction of the time that would be required by a simple brute-force search. As a testbed for the proposed methodology, we used a network simulator for next-generation mmWave cellular systems. After simulating several antenna configurations and collecting the resulting network-level statistics, we feed it into our framework. Results show that, even with few data points, extrapolating a continuous model makes it possible to estimate the global optimum configuration almost instantaneously. The very same tool can then be used to achieve any further optimization goal on the same input parameters in negligible time.