With the prevalence of breast cancer among women and the shortcomings of conventional techniques in detecting breast cancer at its early stages, microwave breast imaging has been an active area of research and has gained momentum over the past few years, mainly due to the advantages and improved detection rates it has to offer. To achieve this outcome, specifically designed antennas are needed to satisfy the needs of such systems where an antenna array is typically used. These antennas need to comply with several criteria to make them suitable for such applications, which most importantly include bandwidth, size, design complexity, and cost of manufacturing. Many works in the literature proposed antennas designed to meet these criteria, but no works have classified and evaluated these antennas for the use in microwave breast imaging. This paper presents a comprehensive study of the different array configurations proposed for microwave breast imaging, with a thorough investigation of the antenna elements proposed to be used with these systems, classified per antenna type, and per the improvements that concern the operational bandwidth, the size of the antenna, the radiation characteristics, and the techniques used to achieve the improvement. At the end of the investigation, a qualitative evaluation of the antenna designs is presented, providing a comparison between the investigated antennas, and determining whether a design is suitable or not to be used in antenna arrays for microwave breast imaging, based on the performance of each. An evaluation of the investigated arrays is also presented, where the advantages and limitations of each array configuration are discussed.

This paper presents a focused and comprehensive literature survey on the use of machine learning (ML) in antenna design and optimization. An overview of the conventional computational electromagnetics and numerical methods used to gain physical insight into the design of the antennas is first presented. The major aspects of ML are then presented, with a study of its different learning categories and frameworks. An overview and mathematical briefing of regression models built with ML algorithms is then illustrated, with a focus on those applied in antenna synthesis and analysis. An in‐depth overview on the different research papers discussing the design and optimization of antennas using ML is then reported, covering the different techniques and algorithms applied to generate antenna parameters based on desired radiation characteristics and other antenna specifications. Various investigated antennas are sorted based on antenna type and configuration to assist the readers who wish to work with a specific type of antennas using ML.

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This paper presents the use of machine learning (ML) to facilitate the design of dielectric-filled Slotted Waveguide Antennas (SWAs) with specified sidelobe levels. Conventional design methods for air-filled SWAs require the simultaneous solving of complex equations to deduce the antenna's design parameters, which typically requires further manual simulation-based optimization to reach the desired resonance frequency and sidelobe level ratio (SLR). The few works that investigated the design of filled SWAs, did not optimize the design for a specified SLR. For an accelerated design process in the case of specified SLRs, we formulate the design of dielectric-filled SWAs as a regression problem where based on input specifications of the antenna's SLR, reflection coefficient, frequency of operation, and relative permittivity of the dielectric material, the developed ML model predicts the filled SWA's design parameters fast and with very low error. These parameters include the unified slots length and the non-uniform slots displacements required to achieve the desired performance. We experiment with several regressive ML algorithms and provide a comparative study of their results. Our numerical evaluations and validation experiments with the best performing ML models demonstrate the high efficiency of the proposed ML approach in estimating the dielectric-filled SWA's design parameters in only a few milliseconds. A comparison to the design obtained through conventional optimization using the Genetic Algorithm also indicate superiority in computation time and resulting antenna performance.