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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.

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.

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Non terrestrial networks (NTN) involving 'in the sky' objects such as low-earth orbit satellites, high altitude platform systems (HAPs) and Unmanned Aerial Vehicles (UAVs) are expected to be integral components of next generation cellular systems. With the deployment of 5G services and beyond, NTNs are leveraged to assist as aerial base stations in providing ubiquitous network connectivity and service to ground users or be deployed as aerial users connected to the cellular network. NTN-aided wireless communication offers multiple benefits such as mobility, flexibility, resistance to ground physical attacks and wide coverage. However, due to their limited resources and the current design of terrestrial cellular systems that do not account for aerial users, and other restrictions such as service requirements, limited available power and storage resources on high-throughput satellites, resource allocation, location of the high altitude platform base station and the flight trajectory of the UAVs need to be intelligently controlled to satisfy various objectives both from an aerial base station and overall network perspectives. To achieve this, many works have explored Reinforcement Learning (RL) techniques to allow aerial platforms in non-terrestrial networks to learn from past observations and achieve some optimal control policy. In this paper and differently from prior surveys, we contribute a comprehensive review of the control objectives required by non-terrestrial platforms that have been solved using RL formulations. We provide an up-to-date overview of the latest applications of RL techniques for different NTN-aided wireless communication aspects. The survey focuses on Markov Decision Process (MDP) formulations in terms of states, actions, and rewards. We synthesize a taxonomy from the surveyed literature and provide a comprehensive representation of the current usages of RL in NTN-aided wireless communications. A qualitative analysis of the level of realism achieved in the works presented in the literature is provided based on several factors that pertain to the simulation environment, station deployment setting, wireless channel assumption, and energy considerations. We also curate a list of challenges that remain to be considered by the research community in order to achieve more efficient deployments and close the simulation-to-reality gap.