In this paper we present the theoretical considerations and the design evolution of a proof-of-concept reconfigurable metasurface, primarily used as a tunable microwave absorber, but also as a wavefront manipulation and polarization conversion device in reflection. We outline the design evolution and all considerations taken into account, from the selection of patch shape, unit cell size, and substrate, to the topology of the structure that realizes the desired tunability. The presented design conforms to fabrication restrictions and is co-designed to work with an integrated circuit chip for providing tunable complex loads to the metasurface, using a commercially available semiconductor process. The proposed structure can perform multiple tunable functionalities by appropriately biasing the integrated circuit: Perfect absorption for a wide range of incidence angles of both linear polarization states, accommodating a spectral range in the vicinity of 5 GHz, with potential also for wavefront control, exemplified via anomalous reflection and polarization conversion. The end vision is for such a design to be scalable and deployable as a practical HyperSurface, i.e., an intelligent multi-functional metasurface capable of concurrent reconfigurable functionalities: absorption, beam steering, polarization conversion, wavefront shaping, holography, and sensing.

The key objective of this paper is to explore the recent state-of-the-art artificial intelligence (AI) applications on the broad field of beamforming. Hence, a multitude of AI-oriented beamforming studies are thoroughly investigated in order to correctly comprehend and profitably interpret the AI contribution in the beamforming performance. Starting from a brief overview of beamforming, including adaptive beamforming algorithms and direction of arrival (DOA) estimation methods, our analysis probes further into the main machine learning (ML) classes, the basic neural network (NN) topologies, and the most efficient deep learning (DL) schemes. Subsequently, and based on the prior aspects, the paper explores several concepts regarding the optimal use of ML and NNs either as standalone beamforming and DOA estimation techniques or in combination with other implementations, such as ultrasound imaging, massive multiple-input multiple-output structures, and intelligent reflecting surfaces. Finally, particular attention is drawn on the realization of beamforming or DOA estimation setups via DL topologies. The survey closes with various important conclusions along with an interesting discussion on potential future aspects and promising research challenges.

This paper introduces a class of programmable metamaterials, whose electromagnetic properties can be controlled via software. These software defined metamaterials (SDMs) stem from utilizing metamaterials in combination with nanonetworks. Metamaterials are artificial structures with properties that may not be found in nature. Since their initial advent, they have inspired ground-breaking applications to a range of research topics, such as electromagnetic invisibility of objects (cloaking), radiation absorption, filtering of light and sound as well as efficient antennas for sensors and implantable communication devices in recent years. However, existing metamaterial structures are "rigid", i.e. they cannot be restructured once constructed. This trait limits their fabrication to some well-equipped laboratories worldwide, slows down innovation, and, most importantly, restricts their applicability to static structures only. The proposed SDMs act as "plastic" (reconfigurable) metamaterials, whose attributes can be changed programmatically via a computer interface. This control is achieved by a network of nanomachines, incorporated into the structure of the metamaterial. The nanomachines may receive commands from the user and perform simple, yet geometrically-altering, actions on the metamaterial profile and tuning of its electromagnetic behavior. Architectural aspects, expected features and implementation issues are covered in this paper, while a suitable nanonetworking model is presented along with simulation results on its anticipated performance. The paper concludes by outlining the research challenges pertaining to the analysis, design, prototyping, manufacturing, and initial application scenarios of the proposed SDMs.

The accurate and fully 3-D analysis of graphene surface conductivity models by means of a frequency-dependent finite-difference time-domain method is introduced in this paper. For the infinite sheet to be consistently simulated, the novel technique uses a set of periodic boundary conditions that lead to a unit cell excited with a spectral scheme in terms of a total-field/scattered-field formulation. On the other hand, graphene itself is modeled through a subcell approach and a complex surface conductivity concept defined by quantum mechanical equations. This conductivity model is next converted to a volume one in order to permit a realistic time-domain study. Numerical outcomes, addressing a variety of applications, reveal a promising coincidence with those acquired from analytical closed-form expressions.

Millimeter-wave bands (around 28, 38, 60, and 73 GHz) are anticipated to play a decisive role in the hosting of future wireless systems. The necessity of smart antennas to adaptively meet the requirements of the wireless links calls for new pattern reconfiguration antennas with beam-steering and/or beam-shaping capabilities. This paper reviews the latest research contributions on pattern reconfigurable antennas at mm-Wave frequencies, proposing an original classification according to the reconfiguration technique and technology. The analyzed systems are divided into two main groups: Reconfigurable and non-reconfigurable feeding antennas. Phased-arrays antennas are the main component of the first category, whereas other reconfigurable means such as the use of metasurfaces or advance materials like liquid crystal or graphene form the second group, devoted to non-reconfigurable feeding antennas. Furthermore, some insights and theoretical background are provided to help the reader understand and appreciate the uniqueness of every solution. Similarly, the beam-scanning and beam-shaping performance of some of the discussed works is analyzed. Finally, some instructive remarks and open research challenges are discussed, with the aim of providing some guidelines for potential new works on the field.