Signal Processing Based Antenna Pattern Characterization for MIMO Systems

TL;DR

This paper introduces a signal processing-based method for antenna pattern characterization in MIMO systems that works in complex environments, eliminating the need for costly anechoic chambers and enabling broader research access.

Contribution

The study presents a novel signal processing approach for antenna pattern characterization that functions effectively in complex environments without specialized measurement equipment.

Findings

Accurately estimates antenna patterns in multi-path environments

Reduces reliance on anechoic chambers for antenna testing

Enables broader access for research and development

Abstract

Sophisticated antenna technologies are constantly evolving to meet the escalating data demands projected for 6G and future networks. The characterization of these emerging antenna systems poses challenges that necessitate a reevaluation of conventional techniques, which rely solely on simple measurements conducted in advanced anechoic chambers. In this study, our objective is to introduce a novel endeavour for antenna pattern characterization (APC) in next-generation multiple-input-multiple-output (MIMO) systems by utilizing the potential of signal processing tools. In contrast to traditional methods that struggle with multi-path scenarios and require specialized equipment for measurements, we endeavour to estimate the antenna pattern by exploiting information from both line-of-sight (LoS) and non-LoS contributions. This approach enables antenna pattern characterization in complex environments without the need for anechoic chambers, resulting in substantial cost savings. Furthermore, it grants a much wider research community the ability to independently perform APC for emerging complex 6G antenna systems, without relying on anechoic chambers. Simulation results demonstrate the efficacy of the proposed novel approach in accurately estimating the true antenna pattern.