In this article, we present the design and validation of an efficient chipless RFID system. A multi-resonator chipless tag is designed and tested for high bit coding capacity. A high gain, ultra-wideband step-shape rectangular patch (USRP) antenna is proposed to validate the specificity of the tag in terms of its operation. The devised antenna is evaluated for various performance parameters, which recommend its suitability for testing and validation of high-capacity tags that can be deployed in modern applications, particularly in the Internet of Things (IoT) networks. A measurement setup is established to achieve performance validation of the tag over a significant range of 40 cm. There is close agreement between the measured and simulated results, which suggests that the proposed antenna system can be adopted in a similar measurement setup to test and validate the performance of any chipless RFID tag operating in the same bandwidth meant for IoT networks.

A compact high‐isolation, four‐port MIMO antenna that is optimized for 5G‐IoT applications and works at the 28 and 39 GHz frequency bands is presented in this paper. The single antenna having a compact size of 6.6 × 6.6 × 0.254 mm ³ is a slot‐etched patch antenna fabricated from Rogers RT Duroid 5880 substrate, which is low loss and designed to carry adequate radiation properties. These properties include a good radiation efficiency of up to 86% and 87% and a suitable comparative gain of up to 7.1 and 6.8 dBi for 28 and 39 GHz, respectively. A compact four‐port orthogonally aligned MIMO antenna with a compact total size of 15.2 × 15.2 × 0.254 mm ³ is proposed after a thorough investigation and exploitation of the single‐antenna element. A simple geometric parasitic element is loaded among the MIMO elements to reduce mutual interaction between antenna elements. The performance of MIMO diversity is thoroughly assessed in terms of many important metrics, such as the envelope correlation coefficient (ECC), diversity gain (DG), mean effective gain (MEG), channel capacity loss (CCL), and total active reflection coefficient (TARC), where strong performance is revealed. The proposed design is significantly more suitable for the 5G‐IoT systems and superior to many other existing designs as a consequence of the results obtained.

This paper proposes a small size meander-line patch antenna which is designed to have biomedical telemetry applications using the Industrial, Scientific and Medical (ISM) band from 2.40 to 2.48 GHz supported by the equivalent circuit model (ECM). Antenna miniaturization is realized by the effective use of several slot structures placed in the rectangular microstrip patch structure, in order to realize electrical length extension and reduce the physical size. The antenna has overall dimensions of 12 × 22 × 0.787 mm³ and is made on a low-loss Arlon AD 450 (ε_r = 4.50 and tanδ = 0.0035) dielectric substrate, which has the desired stable electrical behavior and, importantly, can be used in implantable environments. Experimental validation is done by implanting the fabricated prototype into a laboratory-manufactured tissue-mimicking phantom, and it showed good agreement with simulated results. The designed antenna has a peak gain of 1.29 dBi in free space and −24.99 dBi at a frequency of 2.45 GHz and a fractional impedance bandwidth of about 250 MHz, which will guarantee reliable operation in the face of diversity and fluctuation in the surrounding environment (biological tissues). Furthermore, specific absorption rate (SAR) analysis is carried out in order to comply with international safety standards with peak SAR values kept within the permissible level of 2 W/kg for 10 g averaging tissue. The results show that the proposed antenna provides a good trade-off between the reduction in size, radiation performance and safety to the patient, making it a good candidate for short-range in-body wireless communication, implantable medical devices, and biomedical monitoring systems.

Artificial intelligence (AI) and machine learning (ML) are increasingly being applied to antenna modeling, performance prediction, optimization, inverse de