Machine Learning-aided Design of Thinned Antenna Arrays for Optimized Network Level Performance
This addresses the problem of efficient antenna design for 5G network engineers, representing an incremental improvement by applying existing ML methods to a specific domain bottleneck.
The paper tackles the computational infeasibility of designing thinned antenna arrays for 5G mmWave networks by proposing a machine learning framework that emulates a complex simulator, enabling global optimization over a vast parameter space with reduced time and resources.
With the advent of millimeter wave (mmWave) communications, the combination of a detailed 5G network simulator with an accurate antenna radiation model is required to analyze the realistic performance of complex cellular scenarios. However, due to the complexity of both electromagnetic and network models, the design and optimization of antenna arrays is generally infeasible due to the required computational resources and simulation time. In this paper, we propose a Machine Learning framework that enables a simulation-based optimization of the antenna design. We show how learning methods are able to emulate a complex simulator with a modest dataset obtained from it, enabling a global numerical optimization over a vast multi-dimensional parameter space in a reasonable amount of time. Overall, our results show that the proposed methodology can be successfully applied to the optimization of thinned antenna arrays.