Multi-input multi-output (MIMO) frequency-modulated continuous-wave (FMCW) radar can sense minute body motions, enabling non-contact vital-sign monitoring for applications such as sleep tracking, elderly care, and healthcare. While conventional radar imaging can localize multiple targets, it is limited in distinguishing human subjects from surrounding clutter. In this work, a vital-sign detection and clutter discrimination method using a 77-GHz MIMO FMCW radar is proposed. Range–Doppler maps are constructed by applying a two-dimensional fast Fourier transform (2D FFT) along the fast-time (range) and slow-time (Doppler) dimensions, revealing low-frequency micro-Doppler signatures induced by respiration and heartbeat. Simulations verify that the proposed approach effectively separates human targets from static clutter, and experimental validation using a real radar prototype confirms consistent performance in practical environments. The results demonstrate reliable extraction of vital-sign components and robust human–clutter discrimination, highlighting the feasibility of the method for non-contact bio-sensing applications.

This paper presents a lightweight, free-running Continuous Wave (CW) radar system for non-contact vital sign monitoring with enhanced sensing accuracy through deep learning-based frequency correction. Unlike conventional radar systems requiring complex Frequency-Modulated Continuous Wave (FMCW) architectures or hardware-level stabilization, the proposed system adopts a low-cost CW radar configuration with audio waveform acquisition, enabling compact deployment and scalable data processing. To address the inherent frequency instability of free-running systems, a neural network-based correction scheme is employed. Among several candidate models evaluated, Bidirectional Long Short-Term Memory (Bi-LSTM) model was selected for its superior accuracy and computational efficiency. Experimental results demonstrate that the proposed system achieves over 97% accuracy in respiration and heart rate frequency estimation, with low power consumption and compact size. These characteristics make the system suitable for integration as a sensing block within block-level Integrated Sensing and Communication (ISAC) architectures, particularly in healthcare applications where precise physiological monitoring is critical.

This paper presents a miniaturized, wideband, and optically transparent antenna with high efficiency, designed using the optimized wired metal mesh (WMM) for Internet of Things (IoT) applications. The performance of transparent antennas based on conductive oxides (TCOs) or metal meshes (MMs) is fundamentally determined by the optical transparency (OT) and sheet resistance (SR) of the material. Although OT and SR can be tuned using various fabrication techniques, conventional SR values provided by existing studies do not accurately represent the loss behavior at GHz frequencies. In this paper, an effective SR material model for the WMM is developed with consideration of high-frequency losses and applied in electromagnetic (EM) simulations. Using this model, the proposed antenna is systematically designed to achieve an impedance bandwidth of 66.3% and peak total efficiency of 92.4% over 2.61-5.2 GHz, in a compact 55×40 mm2 form. In addition to intrinsic antenna performance, practical robustness is evaluated under bending deformation, temperature variation, and indoor propagation environments. In the indoor scenarios having multipath effects, Error vector magnitude measurements at 3.5 GHz and 5.15 GHz demonstrated its suitability for IoT communication environments.

This paper presents a 24 GHz circularly polarized (CP) two-dimensional (2D) MIMO FMCW radar front-end based on a scalable aperture-shared array for compact IoT radar imaging. The key contribution is an aperture-shared CP architecture that enables dense Tx/Rx integration within a compact footprint, addressing a fundamental limitation of conventional 2D MIMO radars in which improving angular sensing efficiency under a size-constrained aperture typically requires a larger physical aperture and therefore results in poor spatial efficiency for compact platforms. The proposed design implements a shared-aperture tile that integrates 2 TX and 4 RX channels within a 2.0λ0 × 2.0λ0 aperture, while preserving wide 2D angular coverage and practically sufficient angular discrimination for volumetric sensing tasks relevant to IoT applications. High-isolation operation under dense aperture sharing is achieved through a co-aperture CP antenna structure incorporating a defected ground structure (DGS), which maintains radiation integrity and mitigates Tx-to-Rx leakage and coupling. The proposed architecture also supports scalable tiling of identical aperture-shared tiles to extend the effective 2D aperture while preserving a compact per-tile footprint. The proposed radar is validated through an end-to-end hardware prototype with simulations and measurements, demonstrating reliable multi-target separation and two-dimensional imaging capability.

Advances in indoor positioning technologies have enabled detailed collection and analysis of location data within built environments. However, most existing studies focus on population-level mobility in outdoor contexts, with limited attention to individual-level behaviors in indoor venues. This study proposes a analytical framework utilizing wireless localization technology to model visitor behavior and predict the number of visitors per sector by integrating diverse environmental data. A Feature Tokenizer Transformer-based model is employed to jointly process numerical and categorical variables and effectively learn complex nonlinear relationships. The framework addresses a post-Day-1 operational prediction task, where Day-1 visitor observations are combined with static environmental variables to predict cumulative sector-level visitor distribution across the full exhibition period. The proposed approach is validated using actual data collected from the Korea International Medical & Hospital Equipment Show, comprising 333,470 location records, 210 spatial nodes, and 25 environmental variables. Experimental results show that the proposed model outperforms the second-best model by 8% in terms of predictive accuracy. These results confirm the effectiveness of the proposed approach in accurately predicting visitor distribution in complex indoor environments and highlight its potential for facilitating optimization of spatial management.

This paper presents the design of a dual-polarized Frequency Modulated Continuous Wave (FMCW) radar system equipped with a high-gain lens antenna for the detection of human targets behind walls. The radar system parameters were designed for through-the-wall human detection, and based on these parameters, both the antenna and radar system were developed accordingly. The proposed radar system harnesses the benefits of dual polarization and a compact lens antenna to enhance the signal-to-noise ratio (SNR), a critical parameter for accurate through-the-wall detection. The lens antenna is engineered to provide wideband and high-gain performance in the X-band while maintaining a compact form factor, effectively addressing the conventional trade-offs between antenna size and performance. Furthermore, the application of polarization synthesis markedly enhances the SNR of the received signal, enabling more reliable detection of multiple human targets through the wall. Experimental validation demonstrates the systems efficacy in detecting human targets through a wall. The dual-polarization synthesis technique yields substantial improvements in both SNR and detection accuracy in through-the-wall scenarios.

This article proposes a widebeam coverage antenna solution for a low-complexity millimeter-wave (mmWave) indoor Internet of Things (IoT) network. The proposed switched beamforming antenna (SBA) system eliminates the need for complex feed networks or beamformers by utilizing a single RF switch and multiple radiators. Each radiating element of the antenna is capable of generating multiple main lobe directions, ensuring wide beam coverage and sufficient antenna gain. This design approach effectively addresses the key requirements of distributed antenna system (DAS) configurations, including low complexity, low loss, low power consumption, and cost effectiveness for indoor IoT networks. To validate the effectiveness of the proposed SBA solution, a three-radiator-based SBA was fabricated. The antenna core size was compact, measuring$1.77\,\,\lambda _{0} \times 4.3\,\,\lambda _{0} \times 0.05\,\,\lambda _{0}\,\,(\lambda _{0}$representing the free-space wavelength at 28 GHz). This compact size is more suitable for indoor low-profile networks compared to previously reported SBAs. Experimental measurements revealed peak gains of 5.62, 7.2, and 5.77 dBi for the three different radiation patterns, respectively. Moreover, each beam pattern exhibited a measured 3-dB beam coverage of 101°, 64°, and 106°. The overall 3-dB beamwidth of the system based on the peak gain of the boresight ranged from −66° to 64°, which is wider than previously reported works.

This paper presents a curved-retrodirective beamforming system for improving microwave power transmission efficiency in the Fresnel region. Since microwave power transmission in the far-field region has very low efficiency, studies on the Fresnel region are being actively conducted. In these studies, a retrodirective beamforming (RDB) technique is popular. The RDB system with a sub-array structure was a realistic structure that reduced system complexity. However, the transmission efficiency is lowered because the beamwidth of the transmitter antenna element is narrow. To solve this problem, this paper proposes a curved-retrodirective beamforming system that can focus the microwave power on the receiver. The proposed system uses the peak gain of the transmitter antenna element by using tilted beams to improve transmission efficiency. The system design method that can maximize the transmission efficiency is also presented depending on the given conditions such as transmission distance, characteristics of the transmitter and receiver antenna. The simulation showed a reduction in power leakage compared to the conventional system. The fabrication and measurement validated the efficiency improvement of the proposed system for IoT devices in the Fresnel region.

Body area network considering the external areas of human body is a promising new application. In order to develop efficient body area network system, a communication channel modeling is essential. However, there are few models considering the effect of human body. This paper reports the empirical Ultra wideband(UWB) channel model for body area network. Using the frequency domain measurement system, channel responses in anechoic chamber have been obtained. From the measurement results, the path loss property has been configured to the propagation environments. Furthermore the frequency dependent UWB channel correlation characteristics are investigated.