Wireless Systems Research Articles

MIMO–OFDM based optical millimeter wave generation for radio over fiber wireless communication systems

MIMO–OFDM based optical millimeter wave generation for radio over fiber wireless communication systems

Power-over-fiber-based optical wireless communication systems towards 6G

This paper reports two implementations of power-over-fiber (PoF) solutions applied to radio-over-fiber (RoF) and optical wireless communication (OWC) systems, in the context of an industrial environment. We employ a conventional 62.5-µm multimode fiber (MMF) to deliver optical power to different communication links based on RoF, free-space optics (FSO), and visible light communication (VLC) technologies aiming beyond 5G (B5G) and 6G applications. First, a 3.5-GHz 5G New Radio (5G NR) signal is transmitted throughout a 20-km single-mode optical fiber (SMF) link using RoF technology. Regarding the PoF system, a 5-W optical power is transmitted through a 100-m MMF link. A photovoltaic power converter (PPC) and a DC/DC converter are employed to convert the power from the optical to the electrical domain and adjust the voltage level, respectively, with the purpose of energizing a remote RoF module. The attainable optical and electrical power transmission efficiencies (OPTE and PTE) are 80% and 19%, respectively. Posterior, a second PoF system is implemented to power a hybrid RoF/FSO/VLC B5G system, comprising a 200-m MMF and an additional DC/DC converter. Over 10.5 W of optical power is transmitted to feed an electrical amplifier (EA) and a white LED from the VLC link. In this configuration, we achieve 78% and 18.5% of OPTE and PTE, respectively. Furthermore, a performance investigation based on the root mean square error vector magnitude (EVMRMS) metric is conducted to evaluate the signal using the implemented PoF systems and a conventional electrical power supply. In the first implementation, a throughput of 600 Mbps is achieved with 100-MHz bandwidth without performance degradation, when compared to the conventional-powered RoF system, whereas, in the second implementation, 60-Mbps throughput is achieved when employing the FSO and VLC technologies simultaneously, demonstrating the applicability and potential of the PoF technique for B5G and 6G industrial communications.

Enhancing a loosely coupled inductive wireless power transfer system for LED-driven slurry photocatalytic reactors

To improve the irradiation profile in a conventional photocatalytic reactor, an LED-based light emitter energized by the inductive power transmission (IPT) method has been proposed recently. However, technical challenges such as low coupling due to the asymmetrical wireless coupler, dynamic impedance due to multiple receivers and changing magnetic properties in the transmitter core, and ease in upscaling remain to be addressed. This work proposes an IPT system for an LED-based photocatalytic reactor using a toroidal transformer to match the impedance between a modified Helmholtz transmitter coil and a class-E inverter. A finite element magnetic field simulation and analytical calculation of the induced voltage are used to assess the field uniformity inside the coil. The mutual inductance at selected locations within the coil volume and the power transfer efficiency (PTE) are evaluated. The proposed system requires less than 2 W to generate a uniform magnetic field inside a 10.6 L acrylic cylinder with an induced voltage of 13.9 ± 0.95 Vrms along the cylinder’s central axis. The proposed wireless coupler achieves a coupling coefficient of 0.013–0.034 and a PTE of 23.7%, outperforming similar systems with asymmetrical coupler design while offering a versatile solution for integrating IPT technology into wastewater treatment.

Dual‐frequency three‐dimensional wireless power transfer system to achieve two‐channel independent maximum power transfer

Dual‐frequency three‐dimensional wireless power transfer system to achieve two‐channel independent maximum power transfer

Versatile platform for electrically reconfigurable THz devices based on silicon Schottky-metasurfaces

We propose a versatile platform to design tunable metasurface devices based on Au/n-Si Schottky diodes embedded in a split-ring resonator (SRR) devised on a Si-on-insulator (SOI) wafer. The horizontally formed diodes are connected in the SRR radial direction, reducing the overall junction capacitance of the metasurface array compared to its counterparts with vertically formed Schottky junctions. This reduction in the junction capacitance has an essential role in the switching speed of the metasurface between the On and Off states. By carefully varying the externally applied bias voltage to the Schottky diodes, one can manipulate the incident THz signal at the metasurface resonance frequencies by converting its resonance mode by switching states. We use the forenamed platform to design three fundamental THz devices: a modulator, a polarization switch, and a polarizing beam splitter. A reverse bias of V R =5V excites two LC resonances at 0.3 THz and 0.89 THz in the modulator, which fade away by switching the gate voltage to V F =0.49V, exciting a dipole resonance in the metasurface at 0.75 THz. The numerical results show that this THz modulator enjoys modulation depths of ≥92% at the LC resonances and a phase modulation of ∼1.16rad at 0.86 THz. An identical electric bias change of the Schottky diodes in the polarization switch alters the resonators from anisotropic to isotropic, changing the output wave polarization from circular with nearly 99% of the circular polarization percentage to linear or quasi-linear at four frequencies simultaneously. Additionally, the proposed THz polarization splitter can deflect the cross-polarized transmitted component from the normally outgoing co-polarized one with an angle of 70° at 0.56 THz. The splitting ratio is switched from 1:1 in reverse bias to 14:1 in forward bias by changing the bias to forward bias. We expect that the proposed designs in the THz frequency domain, benefiting from the several hundred GHz switching speed of the Schottky diodes array, will be beneficial in applications such as analysis of the complex organic structures or polarization modulation and polarization-dependent multiplexing/demultiplexing in wireless communication systems.

Wireless Power Transfer System with Constant Voltage/Constant Current Output Performance

Aiming at the problem of unstable output voltage and output current caused by load fluctuation, this paper starts from the perspective of system topology and control strategy for analysis. First, the output characteristics of LCL-S (inductor-capacitor-inductor and series), LCL-P (inductor-capacitor-inductor and parallel), LCL-LCL (inductor-capacitor-inductor and inductor-capacitor-inductor), and LCL-LCC (inductor-capacitor-inductor and inductor-capacitor-capacitor) compensation topologies are analyzed. Combining the advantages of LCL-S and LCL-LCL compensation topology output characteristics, an LCL-LCL/S compensation topology is constructed, and its topology is optimized. Then the influence of parasitic resistance on the output characteristics of LCL-LCL/S compensation topology is derived. A control strategy of primary side regulation is proposed to address the issue of unstable output voltage and current caused by parasitic resistance in the system under variable loads. This method can effectively improve the stability of the output voltage and output current of the wireless power transfer system in the LCL-LCL/S compensation topology under load fluctuations. Finally, a set of experimental prototypes is built to verify the correctness of the theoretical analysis.

Wireless Power Transfer System with Constant Voltage/Constant Current Output Performance

Aiming at the problem of unstable output voltage and output current caused by load fluctuation, this paper starts from the perspective of system topology and control strategy for analysis. First, the output characteristics of LCL-S (inductor-capacitor-inductor and series), LCL-P (inductor-capacitor-inductor and parallel), LCL-LCL (inductor-capacitor-inductor and inductor-capacitor-inductor), and LCL-LCC (inductor-capacitor-inductor and inductor-capacitor-capacitor) compensation topologies are analyzed. Combining the advantages of LCL-S and LCL-LCL compensation topology output characteristics, an LCL-LCL/S compensation topology is constructed, and its topology is optimized. Then the influence of parasitic resistance on the output characteristics of LCL-LCL/S compensation topology is derived. A control strategy of primary side regulation is proposed to address the issue of unstable output voltage and current caused by parasitic resistance in the system under variable loads. This method can effectively improve the stability of the output voltage and output current of the wireless power transfer system in the LCL-LCL/S compensation topology under load fluctuations. Finally, a set of experimental prototypes is built to verify the correctness of the theoretical analysis.

Circularly polarized beam-scanning leaky wave antenna based on a slow-wave substrate integrated waveguide

A circularly polarized (CP) beam-scanning leaky wave antenna (LWA) based on a slow-wave substrate integrated waveguide (SW-SIW) is presented in this paper. The SW effect is realized by introducing a spoof surface plasmon polariton (SSPP), excited by periodic etched 45° tilted slots on the top metallic layer of an SIW. With extra delta modulation, the electromagnetic waves radiate through the etched slots and scan continuously from a backward to forward direction as the frequency changes. Taking advantage of the tilted slots and relative displacement between the upper and lower slots, the direction of the electric field of the radiated waves and phase difference between them are adjustable, making a CP characteristic available. Both the simulated and measured results show the proposed LWA exhibits good performance. The total scanning angle reaches 70°, and the average gain is 8.9 dBi from 9.5 GHz to 11.7 GHz. The CP characteristic is verified through the axial ratio of less than 3 during the operating frequency range. A simple structure, compact size, and good performance make the proposed design promising for a compact wireless communication system.

Automatic Clinical Assessment of Swallowing Behavior and Diagnosis of Silent Aspiration Using Wireless Multimodal Wearable Electronics.

Dysphagia is more common in conditions such as stroke, Parkinson's disease, and head and neck cancer. This can lead to pneumonia, choking, malnutrition, and dehydration. Currently, the diagnostic gold standard uses radiologic imaging, the videofluoroscopic swallow study (VFSS); however, it is expensive and necessitates specialized facilities and trained personnel. Although several devices attempt to address the limitations, none offer the clinical-grade quality and accuracy of the VFSS. Here, this study reports a wireless multimodal wearable system with machine learning for automatic, accurate clinical assessment of swallowing behavior and diagnosis of silent aspirations from dysphagia patients. The device includes a kirigami-structured electrode that suppresses changes in skin contact impedance caused by movements and a microphone with a gel layer that effectively blocks external noise for measuring high-quality electromyograms and swallowing sounds. The deep learning algorithm offers the classification of swallowing patterns while diagnosing silent aspirations, with an accuracy of 89.47%. The demonstration with post-stroke patients captures the system's significance in measuring multiple physiological signals in real-time for detecting swallowing disorders, validated by comparing them with the VFSS. The multimodal electronics can ensure a promising future for dysphagia healthcare and rehabilitation therapy, providing an accurate, non-invasive alternative for monitoring swallowing and aspiration events.

Automatic Clinical Assessment of Swallowing Behavior and Diagnosis of Silent Aspiration Using Wireless Multimodal Wearable Electronics.

Dysphagia is more common in conditions such as stroke, Parkinson's disease, and head and neck cancer. This can lead to pneumonia, choking, malnutrition, and dehydration. Currently, the diagnostic gold standard uses radiologic imaging, the videofluoroscopic swallow study (VFSS); however, it is expensive and necessitates specialized facilities and trained personnel. Although several devices attempt to address the limitations, none offer the clinical-grade quality and accuracy of the VFSS. Here, this study reports a wireless multimodal wearable system with machine learning for automatic, accurate clinical assessment of swallowing behavior and diagnosis of silent aspirations from dysphagia patients. The device includes a kirigami-structured electrode that suppresses changes in skin contact impedance caused by movements and a microphone with a gel layer that effectively blocks external noise for measuring high-quality electromyograms and swallowing sounds. The deep learning algorithm offers the classification of swallowing patterns while diagnosing silent aspirations, with an accuracy of 89.47%. The demonstration with post-stroke patients captures the system's significance in measuring multiple physiological signals in real-time for detecting swallowing disorders, validated by comparing them with the VFSS. The multimodal electronics can ensure a promising future for dysphagia healthcare and rehabilitation therapy, providing an accurate, non-invasive alternative for monitoring swallowing and aspiration events.

Innovative techniques for achieving flat response in a dual-resonance ultra-wideband low-noise amplifier

This paper introduces a two-stage ultra-wideband low-noise amplifier (UWB-LNA) intended to be used in wireless communication systems. The architecture uses a novel double-resonance load network in the first stage and resistive shunt feedback in the second stage to achieve wide bandwidth with a flat response. A common gate stage at the input port seeks to present a high-impedance load to the single resonant network, while concurrently shunt negative feedback, source degeneration, and cascoded feedback schemes are used to improve performance. In this respect, the cascoded feedback provides flat gain across a wide bandwidth, while the source degeneration helps in impedance matching. Post-layout foot-print for the UWB-LNA designed and simulated using Cadence Virtuoso 180nm technology is 0.532 mm2 with an operating frequency 3.1–10.6 GHz incorporated. Operating on a 1.8 V supply voltage, it consumes 6 mW of power. The amplifier achieves a maximum gain of 18.75 dB, maintaining a flat low noise figure of 3.15 dB across frequencies ranging from 3.1 to 10.6 GHz. Stability analysis using the Roulettes test confirms the reliability of the proposed LNA, with Kf > 1 and Δ < 1.

A Comprehensive Review of Radio Signal Propagation Prediction for Terrestrial Wireless Communication Systems

The subject of study known as radio propagation prediction refers to predicting the behaviour and characteristics of radio waves as they propagate through the atmosphere. It is a basic element of all wireless communication systems, including satellite communications, broadcasting and cellular networks. While there is no study covers all the techniques used to predict the radio signal prediction, this study presents a review of the propagation prediction models between 2018-2023 used for terrestrial wireless communication systems; the classic empirical models were briefly explained, followed by the deterministic propagation models that have been developed using ray-tracing with deep and machine learning techniques. Recent studies on an improvement of the computational efficiency and accuracy of propagation prediction models were also reviewed, in addition to an overview of some of the traditional statistical models. Furthermore, some of the new techniques in propagation prediction were described. The results of these studies explain that techniques using ray tracing produce better results than other techniques.

A Comprehensive Review of Radio Signal Propagation Prediction for Terrestrial Wireless Communication Systems

The subject of study known as radio propagation prediction refers to predicting the behaviour and characteristics of radio waves as they propagate through the atmosphere. It is a basic element of all wireless communication systems, including satellite communications, broadcasting and cellular networks. While there is no study covers all the techniques used to predict the radio signal prediction, this study presents a review of the propagation prediction models between 2018-2023 used for terrestrial wireless communication systems; the classic empirical models were briefly explained, followed by the deterministic propagation models that have been developed using ray-tracing with deep and machine learning techniques. Recent studies on an improvement of the computational efficiency and accuracy of propagation prediction models were also reviewed, in addition to an overview of some of the traditional statistical models. Furthermore, some of the new techniques in propagation prediction were described. The results of these studies explain that techniques using ray tracing produce better results than other techniques.

MULTI-HOP RELAY SELECTION FOR COOPERATIVE SENSING IN COGNITIVE RADIO NETWORKS

Spectrum sensing is one of the essential blocks of the cognitive radio (CR) system. Cooperative spectrum sensing (CSS) enhances sensing performance by exchanging information among secondary users (SUs). The paper addresses a situation where some SUs cannot communicate their local information with the fusion center (FC) due to real-time circumstances, i.e., shadowing, large distances, increased signal interference, etc. The issue can be resolved by introducing relay nodes to assist such SUs in transmitting local information to the corresponding destination (FC), making relay selection an essential component on which the system's performance depends. This paper proposes a critical path method (CPM) based multi-hop relay selection technique to select a set of relay nodes combined to form a relay with minimum path loss. The performance of the proposed scheme is compared with that of the existing methodologies to demonstrate its robustness and efficiency in terms of end-to-end path loss and reliability. In addition, a closed-form expression of outage probability is also developed to withstand the results.

Antagonistic strategy for enhanced performance polyimide-based triboelectric nanogenerators and wireless self-powered sensors in extremely harsh environments

Triboelectric nanogenerators (TENGs) offer promising green energy solutions for portable microelectronics in the Internet of Everything era. However, their practical applications are limited by low electrical output and stability in harsh environments. This study introduces an innovative antagonistic strategy to enhance TENG performance and stability using thermo-stable polyimide with customizable porous structures (ip-PI). By employing antagonistic solubility, we create ip-PI using chloroform as a solvent for PI and acetone as a solvent for the Poly(vinylidene fluoride-co-hexafluoropropylene) (PVDF-co-HFP) template, whereas they both act as nonsolvents for their respective counterparts. Additionally, microbeads-patterned chitosan (mb-CS) is rationally designed and fabricated as morphologically and electrically antagonistic counter surfaces for ip-PI. The resulting antagonist-inspired TENG (AI-TENG) constructed from ip-PI and mb-CS achieves a remarkable average power density of 4 W·m−2, presenting an outstanding 200-fold increase in power compared to TENG made from unstructured flat surfaces. Furthermore, the ip-PI demonstrates excellent temperature resistance, enabling the TENG to operate at extremely high temperatures up to 250 °C, with promising durability of over 10,000 contact-separation cycles. Applications include self-powered machinery condition monitoring sensors and a wireless communication fire forest pre-warning system, showcasing significant advancements in flexible, high-performance TENGs suitable for industrial use in extreme environments.

Optimizing energy harvesting with diversity in cooperative simultaneous wireless information and power transfer systems

Optimizing energy harvesting with diversity in cooperative simultaneous wireless information and power transfer systems

Deep Learning-Based Hybrid Precoding Approach in the Massive Multiple-Input Multiple-Output System

Precoding is a critical signal processing technique used in wireless communication systems to enhance transmission performance. This paper initially provides a brief overview of various conventional precoding algorithms. The exploration includes various nonlinear precoding algorithms which outperform linear techniques in high signal-to-noise ratio (SNR) scenarios. Moreover, the paper delves into practical considerations and provides insights into selecting the most suitable technique for specific communication scenarios. The deep learning-based hybrid precoder is designed using the MDL-AltMin algorithm and simulated results show a spectral efficiency of (SE) 40.96 dB compared with different precoding algorithms. The precoder model is modified with a Hybrid Precoding Net algorithm with a spectral efficiency of 21.07 dB for high values of SNR such as −30 and again compared with deep learning precoders. The HPNet model is proposed with a spectral efficiency of 23.04 dB with and without CSI (Channel State Information) at the transmitter. Furthermore, the HPNet model is compared with optimal digital precoders.

Portable multi-focal visual evoked potential diagnostics for multiple sclerosis/optic neuritis patients

PurposeMultiple sclerosis (MS) is a neuro-inflammatory disease affecting the central nervous system (CNS), where the immune system targets and damages the protective myelin sheath surrounding nerve fibers, inhibiting axonal signal transmission. Demyelinating optic neuritis (ON), a common MS symptom, involves optic nerve damage. We’ve developed NeuroVEP, a portable, wireless diagnostic system that delivers visual stimuli through a smartphone in a headset and measures evoked potentials at the visual cortex from the scalp using custom electroencephalography electrodes.MethodsSubject vision is evaluated using a short 2.5-min full-field visual evoked potentials (ffVEP) test, followed by a 12.5-min multifocal VEP (mfVEP) test. The ffVEP evaluates the integrity of the visual pathway by analyzing the P100 component from each eye, while the mfVEP evaluates 36 individual regions of the visual field for abnormalities. Extensive signal processing, feature extraction methods, and machine learning algorithms were explored for analyzing the mfVEPs. Key metrics from patients’ ffVEP results were statistically evaluated against data collected from a group of subjects with normal vision. Custom visual stimuli with simulated defects were used to validate the mfVEP results which yielded 91% accuracy of classification.Results20 subjects, 10 controls and 10 with MS and/or ON were tested with the NeuroVEP device and a standard-of-care (SOC) VEP testing device which delivers only ffVEP stimuli. In 91% of the cases, the ffVEP results agreed between NeuroVEP and SOC device. Where available, the NeuroVEP mfVEP results were in good agreement with Humphrey Automated Perimetry visual field analysis. The lesion locations deduced from the mfVEP data were consistent with Magnetic Resonance Imaging and Optical Coherence Tomography findings.ConclusionThis pilot study indicates that NeuroVEP has the potential to be a reliable, portable, and objective diagnostic device for electrophysiology and visual field analysis for neuro-visual disorders.

Initial Study of the Onsite Measurement of Flow Sensors on Turbine Blades (SOTB)

This paper presents a new framework using MEMS flow sensors on turbine blades (SOTB) to investigate unsteady flow features of a rotating wind turbine. Self-heating flow sensors were implemented by the U18 complementary metal-oxide semiconductor (CMOS) MEMS foundry provided by Taiwan Semiconductor Research Institute (TSRI). Flow sensor chips with a size of 1.5 mm × 1.5 mm were parylene-coated, output via a wireless data acquisition system (WDAQ), and mounted at the root, middle and tip of a 1.2 m diameter semi-rigid turbine blade of a 400 W horizontal axis wind turbine (HAWT). The instantaneous angles of attack (AOAs) of the SOTB were found to be 46~62°, much higher than the general stall AOA of 15°, but were accurate considering the normal detection of the flow sensors. The computational fluid dynamics (CFD) simulation of the HAWT was also compared with the SOTB output. The onsite measurement herein revealed that the 3D secondary flow increment, mostly obvious near the middle part of the turbine blades, degraded both the sensor and the turbine performance and initially justified the onsite measurement application.

High‐Performance Textile‐Based Capacitive Strain Sensors via Enhanced Vapor Phase Polymerization of Pyrrole and Their Application to Machine Learning‐Assisted Hand Gesture Recognition

Sensors based on everyday textiles are extremely promising for wearable applications. The present work focuses on high‐performance textile‐based capacitive strain sensors. Specifically, a conductive textile is obtained via vapor‐phase polymerization of pyrrole, in which the usage of methanol co‐vapor and the addition of imidazole to the iron chloride oxidant solution are shown to maximize conductivity. A technique to provide insulation and mechanical resistance using thermoplastic polyurethane and polystyrene‐block‐polyisoprene‐block‐polystyrene/barium titanate composite is developed. Such insulated conductive elastics are then used to fabricate highly sensitive twisted yarn capacitive sensors. A textile glove is subsequently embedded with such sensors. The wireless measurement and transmission system demonstrate efficacy in capturing capacitance variations upon strain and monitoring hand motions. A machine learning model to recognize 12 gestures is implemented—100% classification accuracy is obtained.