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Low-profile metasurface antennas are proposed for cellular base station applications. The dual-slant crossed-dipole antennas with four resonances employ both transverse magnetic (TM) and transverse electric (TE) surface wave modes. The metasurface dipole arms are capacitively coupled by an electrically small crossed-feed in a low-cost single substrate. The metasurface layer consists of $n \times n$ element arrays. The proposed low-profile ( $0.14\lambda $ ) antennas can achieve a 10-dB impedance bandwidth of 57%, 58%, and 84% for metasurface dipole arms comprising $2\times 2$ , $3\times 3$ , and $4\times 4$ elements, respectively. For cellular 65° sector base station applications where 14 dB is required, a bandwidth of 51%, 52%, and 63% for $2\times 2$ , $3\times 3$ , and $4\times 4$ elements, respectively, is realized. For a single dipole with a reflector, the average boresight gain is 9 dBi, 3-dB beamwidth is $62^{\circ }~\pm ~7^{\circ }$ , front-to-back ratio is better than 25 dB, and isolation is better than 35 dB over the frequency range of 1.7–2.7 GHz. For user equipment (UE) applications, extra low-profile ( $0.11\lambda $ ) antennas are also presented.

The Internet of Things (IoT) has already ingrained itself into our daily lives, with the number of connected devices that are growing rapidly. Particularly, low-power wireless sensing devices are anticipated to make significant contributions to this expansion. These compact devices are designed to operate for an extended duration, spanning years or even decades, but the growing demand for such devices poses challenges in terms of ensuring sustainable power supply. To sustainably power these devices, ambient radio-frequency (RF) energy harvesting has emerged as a possible approach. However, placing the harvester in an optimal location is essential to maximize the reception of ambient RF energy and ensure reliable performance. In this article, we investigate the estimation of the ideal location for RF energy harvesting by utilizing machine-learning (ML) techniques in real-world scenarios. The study involves a frequency-dependent analysis and a received signal intensity analysis. A comparison of three different interpolation methods with five supervised ML algorithms is conducted, and the effect of reduced measurement points on estimation accuracy is evaluated. The outcomes demonstrate how well ML estimates the optimal location for energy scavenging and offer insights into creating sustainable energy systems.

In this article, a compact 3-D beam-steering MIMO antenna operating at 5.75 GHz is proposed for on-body Internet-of-Things (IoT) applications. The planar antenna comprises a central patch and two concentric annular rings. The theory of spherical modes is used to discuss the beam-steering concept, where the free space measured beam-scanning covers the entire ${xy}$ plane, while a 44° range is realized in ${x{z}}$ and ${y{z}}$ planes. The on-body performance is tested using a multilayer phantom, and the measured peak realized gain is 6.41 dBi. The antenna is characterized by compact size ( $0.77{\lambda }$ ), low profile ( $0.03{\lambda }$ ), Specific Absorption Rate (SAR) below the established limits, good efficiency, and 3-D beam-steering characteristics while operating in an on-body setup. Therefore, the proposed solution can enable advanced wireless applications like localization and physical layer security in emerging size-constrained on-body IoT devices.