Antennas and Propagation Research From Large-Scale Unstructured Data With Machine Learning: A review and predictions

Abstract

The past century has witnessed remarkable progress in antennas and propagation (A&P) research, which has made dramatic changes to our society and life and has led to paradigm shifts in engineering and technology. Although the underlying theory of electromagnetics is well established and mature, research on A&P will continue to play a paramount role in the Fourth Industrial Revolution. In this article, we present an approach based on natural language processing (NLP) and machine learning (ML) techniques to review A&P research based on large-scale unstructured data from openly published scientific papers and patents and, in turn, provide meaningful summative and predictive information. We particularly screen 159,000 research papers published between 1981 and 2021 and extract a pool of 2,415 significant keywords reflecting past and present key research topics in A&P. We then apply an encoder-decoder long short-term memory (LSTM) network with an integrated attention mechanism to predict the future trends of A&P research in the form of a Gartner's hype cycle.