Wireless Systems Research Articles

Wireless Neural Recording Systems With Fully Embedded Components in the Package

Wireless Neural Recording Systems With Fully Embedded Components in the Package

Context-Aware Prediction with Secure and Lightweight Cognitive Decision Model in Smart Cities

Cognitive networks with the integration of smart and physical devices are rapidly utilized for the development of smart cities. They are explored by many real-time applications such as smart homes, healthcare, safety systems, and other unpredictable environments to gather data and process network requests. However, due to the external conditions and inherent uncertainty of wireless systems, most of the existing approaches cannot cope with routing disturbances and timely delivery performance. Further, due to limited resources, the demand for a secure communication system raises another potential research challenge to protect sensitive data and maintain the integrity of the urban environment. This paper presents a secured decision-making model using reinforcement learning with the combination of blockchain to enhance the degree of trust and data protection. The proposed model increases the network efficiency for resource utilization and the management of communication devices with the alliance of security. It provides a reliable and more adaptive paradigm by exploring learning techniques for dealing with the intrinsic uncertainty and imprecision of cognitive systems. Also, the incorporation of blockchain technology reduces the risk of a single point of failure, malicious vulnerabilities, and data leakage, ultimately fostering trust for urban sensor applications. It validates the incoming routing links and identifies any communication fault incurred due to malicious interference. The proposed model is rigorously tested and verified using simulations and its significance has been proven for network metrics in comparison to existing solutions.

A novel multi-user collaborative cognitive radio spectrum sensing model: Based on a CNN-LSTM model

Cognitive Radio (CR) technology enables wireless devices to learn about their surrounding spectrum environment through sensing capabilities, thereby facilitating efficient spectrum utilization without interfering with the normal operation of licensed users. This study aims to enhance spectrum sensing in multi-user cooperative cognitive radio systems by leveraging a hybrid model that combines Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. A novel multi-user cooperative spectrum sensing model is developed, utilizing CNN’s local feature extraction capability and LSTM’s advantage in handling sequential data to optimize sensing accuracy and efficiency. Furthermore, a multi-head self-attention mechanism is incorporated to improve information flow, enhancing the model’s adaptability and robustness in dynamic and complex environments. Simulation experiments were conducted to quantitatively evaluate the performance of the proposed model. The results demonstrate that the CNN-LSTM model achieves low sensing error rates across various numbers of secondary users (16, 24, 32, 40, 48), with a particularly low sensing error of 9.9658% under the 32-user configuration. Additionally, when comparing the sensing errors of different deep learning models, the proposed model consistently outperformed others, showing a 12% lower sensing error under low-power conditions (100 mW). This study successfully develops a CNN-LSTM-based cooperative spectrum sensing model for multi-user cognitive radio systems, significantly improving sensing accuracy and efficiency. By integrating CNN and LSTM technologies, the model not only enhances sensing performance but also improves the handling of long-term dependencies in time-series data, offering a novel technical approach and theoretical support for cognitive radio research. Moreover, the introduction of the multi-head self-attention mechanism further optimizes the model’s adaptability to complex environments, demonstrating significant potential for practical applications.

Design and Implementation of a Wireless Power Transfer System Using LCL Coupling Network with Inherent Constant-Current and Constant-Voltage Output for Battery Charging

The constant current followed by constant voltage (CC-CV) charging method is commonly employed for battery charging, effectively extending battery life and reducing charging time. However, as charging progresses, the battery’s internal resistance increases, complicating the charging circuit. This paper designs a wireless power transfer system utilizing an LCL coupling network for battery charging. The system employs frequency modulation (FM) to manage its CC and CV output characteristics at two fixed frequency points. The influence of the LCL coupling network parameters on system output characteristics was investigated using MATLAB. A simulation model was developed in the PSIM environment to validate the CC and CV output characteristics. The simulation results show that the system has load-independent constant-current and constant-voltage characteristics at two different frequency points. In order to verify the theoretical analysis, an experimental platform was also established. Experimental results demonstrate that the proposed system operates effectively in CC mode, maintaining constant output current across various loads, while in CV mode, it effectively regulates output voltage for different loads. The designed frequency modulation controller ensures a rapid response to sudden changes in load resistance, regardless of operating in CC or CV modes.

Enhancing covert communication in NOMA systems with joint security and covert design.

The explosion of Internet-of-Thing enables several interconnected devices but also gives rise chance for unauthorized parties to compromise sensitive information through wireless communication systems. Covert communication therefore has emerged as a potential candidate for ensuring data privacy in conjunction with physical layer transmission to render two lines of defense. In this paper, we aim to enhance the individual transmission of nearby users in non-orthogonal multiple access (NOMA) systems under scenarios of an eavesdropper who monitors covert transmission before decoding covert information. For this problem, we first provide a comprehensive analysis of the NOMA system in terms of outage probability (OP), secrecy outage probability (SOP), and detection error probability (DEP), where all of them are quantified in exact and asymptotic closed-form expressions. Besides, we have also derived closed-form formulas for users' covert and public rates. Under the system requirements of the maximal OP and SOP and the minimal DEP, we formulate the optimization of resource power allocation to: 1) minimize the OP of covert communication and 2) maximize the covert rate. Thanks to the developed analytical expressions, we obtain closed-form expressions for the sub-optimal power allocation coefficient for each problem. Simulation results validate the efficacy of the analytical mathematical frameworks and reveal that the proposed approaches of power allocation can provide attractive performance improvement compared to fixed power allocations only.

Modulation pattern recognition method of wireless communication automatic system based on IABLN algorithm in intelligent system.

The aim of this study is to address the limitations of convolutional networks in recognizing modulation patterns. These networks are unable to utilize temporal information effectively for feature extraction and modulation pattern recognition, resulting in inefficient modulation pattern recognition. To address this issue, a signal modulation recognition method based on a two-way interactive temporal attention network algorithm has been developed. A two-way interactive temporal network is designed on the basis of the long and short-term memory network with the objective of enhancing the contextual connection of the temporal network. The output of the temporal network is attentively weighted using the soft attention mechanism. The proposed algorithm exhibited enhanced overall, average, and maximum recognition rates at varying signal-to-noise ratios, with an increase of 10.34%, 8.33%, and 3.33%, respectively, in comparison to other algorithms within the Radio Machine Learning (RML) 2016.10b dataset. Furthermore, the modulated signal recognition accuracy was as high as 92.84%, with an average increase in the Kappa coefficient of 12.28%. The Kappa coefficient in the Communication Signal Processing Benchmark for Machine Learning (CSPB.ML2018) 2018 dataset was 0.62, representing an average increase of 10.32% over other algorithms. The results demonstrate that the proposed recognition method can enhance the network's accuracy in recognizing modulated signals. Moreover, it has potential applications in modulation pattern recognition in automatic systems for wireless communications.

A design method to optimize GaN-based class E resonant inductive Wireless Power Transfer systems – application to non-mating connectors

Abstract Wireless Power Transfer (WPT) systems have demonstrated their efficiency over a broad frequency spectrum. However, the design and the optimization of multi-MHz frequency WPT systems remain complex due to parasitic components specifically. This paper outlines a methodology for designing and optimizing a compact 13.56 MHz resonant inductive coupling WPT systems based on a GaN Class E inverter. The proposed design approach combines analytical modeling and advanced numerical simulations taking into account parasitic elements. The effectiveness of this approach is experimentally confirmed through a prototype, which shows an efficiency of 78% for a transmitted power of 43 W to a 5.54 cm³ coupler. The advanced modeling results perfectly match with the experimental ones (mismatch of only 2% for the evaluation of efficiency). The WPT system developed thanks to this method shows a power density among the highest ones in the non-mating connectors’ state of the art.

Low-Power Wake-Up Receivers for Resilient Cellular Internet of Things

Smart Cities leverage large networks of wirelessly connected nodes embedded with sensors and/or actuators. Cellular IoT, such as NB-IoT and 5G RedCap, is often preferred for these applications thanks to its long range, extensive coverage, and good quality of service. In these networks, wireless communication dominates power consumption, motivating research on energy-efficient yet resilient and robust wireless systems. Many IoT use cases require low latency but cannot afford high-power radios continuously operating to accomplish this. In these cases, wake-up receivers (WURs) are a promising solution: while the high-power main radio (MR) is turned off/idle, a lightweight WUR is continuously monitoring the RF channel; when it detects a wake-up sequence, the WUR will turn on the MR for subsequent communications. This article provides an overview of WUR hardware design considerations and challenges for 4G and 5G cellular IoT, summarizes the recent 3GPP activities to standardize NB-IoT and 5G wake-up signals, and presents a state-of-the-art WUR chip.

Design of multi-stage spectrum sensing via layer customized convolutional neural network for cooperative communication

ABSTRACT Cognitive radios make extensive use of cooperative spectrum sensing to make optimum use of the accessible spectrum. Multi-stage spectrum sensing in cooperative communication refers to a technique used in cognitive radio systems where multiple stages of sensing are employed to improve the accuracy spectrum sensing. Accordingly, this paper proposes a unique design of ISDP-AL-CNN-based multi-stage spectrum sensing for cooperative communication. The multi-stage spectrum sensing is performed under two stages: (i) Determining the presence of PU and (ii) Detect the availability of unused radio spectrum. In stage 1, the energy detection approach is performed for each received signal and then compare the energy detected signal with threshold to determine whether PU is present or not. In stage 2, the features such as SNR and PSD are extracted from the received signal and subjected to obtain feature vector and fed the formulated feature into Fusion centre to extract intense features. Further, the retrieved features are given to the ISDP-AL-CNN model to train the features. The ISDP-AL-CNN model adopts ISDP-based attention layer for better performance of the training network. Moreover, it uses SVD to set the data label in the ISDP-AL-CNN model and proficiently detects the spectrum availability in the CR-IoT network.

Compact diplexer based on SIR feeders, T-shaped resonators, and UIR components for mobile wireless systems

Utilizing T-shaped resonators, Uniform Impedance Resonator (UIR) and Step Impedance Resonator (SIR) feeders, this research presents a new compact microstrip diplexer developed for wireless networks. The diplexer's frequency responses with narrowband features reduce interference and enhance signal integrity, which are critical for current communication systems, and it targets channel frequencies of 3.2 and 4.7 GHz. It exhibits insertion losses of 0.3 and 0.7 dB, return losses of 30 and 19 dB, and isolation between channels surpassing 30 dB, guaranteeing minimum cross-talk and robust signal transmission. Modern mobile environments rely on quick signal processing and data transmission speeds, which are improved by incorporating negative group delay (NGD). The diplexer has been low-profile and suitable for devices with limited space due to its compact design and construction on an FR4 substrate. It is a viable option for mobile communication applications, as confirmed by thorough simulations and experimental validations.

Nonpolar P-type Conjugated Small Molecules Enable High-Performance Organic Photodetectors for Potential Application in Optical Wireless Communication.

The high responsivity and broad spectral sensitivity of organic photodetectors (OPDs) present a bright future of commercialization. However, the relatively high dark current density still limits its development. Herein, two novel nonpolar p-type conjugated small molecules, NSN and NSSN, are synthesized as interface layers to enhance the performance of the OPDs, which not only can tune energy alignments and increase the reverse charge injection barrier but also can reduce the interfacial trap density. Moreover, benefiting from the smoother surface morphology and enhanced conductivity, the NSN exhibited superior charge transport and collection properties. Consequently, the OPD with NSN achieved a dark current density of 0.37 nA cm-2 and a high specific detectivity of 2.77 × 1013 Jones at -2 V. More importantly, the optimized OPDs can be successfully integrated into optical communication systems, demonstrating precise digital signal communication without obvious distortion, showing promising application potential in the wireless transmission system.

Wireless microwave-to-optical conversion via programmable metasurface without DC supply

Microwave-optical interaction and its effective utilization are vital technologies at the frontier of classical and quantum sciences for communication, sensing, and imaging. Typically, state-of-the-art microwave-to-optical converters are realized by fiber and circuit approaches with multiple processing steps, and external powers are necessary, which leads to many limitations. Here, we propose a programmable metasurface that can achieve direct and high-speed free-space microwave-to-laser conversion. Moreover, it supports reverse conversion, achieving bidirectional operations. The programmable metasurface converter is realized by integrating subwavelength microwave resonant structures, MS junction and photoelectric PN junction components together, without connecting any direct-current supplies to provide driving bias. We further demonstrate the enormous potentials of the metasurface converter in cross-media links and develop a full-duplex air-water wireless communication system. Experimental results show that the bidirectional real-time data transmissions and exchanges are established through the air-water boundary. This work represents a decisive step towards microwave-optical interconversion on wireless and battery-free interfaces.

Clinical validation of a wireless patch-based polysomnography system.

Onera Health has developed the first wireless, patch-based, type-II PSG system, the Onera Sleep Test System (STS), to allow studies to be performed unattended at the patient's home or in any bed at a medical facility. The goal of this multicenter study was to validate data collected from the patch-based PSG to a traditional PSG for sleep staging and AHI. Simultaneous traditional PSG and patch-based PSG study data were obtained in a sleep laboratory from 206 participants with a suspected sleep disorder recruited from 7 clinical sites. Blinded, randomized scoring of the traditional PSG and patch-based PSG recordings was completed according to The AASM Manual for the Scoring of Sleep and Associated Events, version 2.6 criteria by three independent scorers. Concordance correlation coefficients were high between the patch-based device and traditional PSG across essential sleep and respiratory variables - TST (0.87); Wake (0.84); NREM (0.80); N1 (0.72); N2 (0.71); N3 (0.64); REM (0.80) and AHI (0.94). There was substantial agreement between epoch sleep staging scored on the patch-based device and traditional PSG (average Cohen's kappa of 0.62 ± 0.13 across all scorers). The patch-based type-II PSG had a similar performance on sleep staging and respiratory variables when compared to Traditional PSG, thus making it possible to use the patch-based PSG for a routine PSG study. These results open the possibility of performing unattended PSG studies efficiently and accurately outside the sleep laboratory improving access to high quality sleep assessments for patients with sleep disorders. Registry: ClinicalTrials.gov; Identifier: NCT05310708.

Deep Learning-based Signal Identification in Wireless Communication Systems: A Comparative Analysis on 3G, LTE, and 5G Standards

Deep Learning-based Signal Identification in Wireless Communication Systems: A Comparative Analysis on 3G, LTE, and 5G Standards

A novel optimized framework for improving the quality of services in mobile wireless system

A novel optimized framework for improving the quality of services in mobile wireless system

Towards Optimizing the Downlink Transmit Power in UAV-Integrated IRS Wireless Systems

Air-to-ground interference poses a critical challenge in integrating unmanned aerial vehicles (UAVs) into cellular networks. In downlink scenarios, UAVs can withstand significant interference from co-channel base stations (BSs) due to the guaranteed line-of-sight (LoS) connection with ground users. Our research focuses on power optimization in BSs and applying green energy principles to pave the way for more sustainable and energy-efficient BSs within UAV-integrated wireless systems. To this end, this paper investigates a downlink UAV communication scenario in which the intelligent reflecting surface (IRS) is mounted on the UAV to practically nullify the interference originating from the co-channel BSs. We formulate the IRS beamforming matrix to reduce transmit power by optimizing passive beamforming for IRS elements, incorporating adjustments to phase shifts and amplitude coefficients while considering the positioning of the UAV. The proposed optimization problem is non-convex, and thus a successive convex approximation (SCA) method is adopted to convert all constraints to a quadratic approximation. Simulation results demonstrate that the proposed SCA algorithm provides an efficient transmit power minimization approach with low computational complexity for large IRS since it achieves close-to-optimal performance, and significantly outperforms conventional systems without IRS. In interference scenarios and with different numbers of IRS meta-atoms, the proposed algorithm achieves a power reduction of approximately 8 and 13 dBm, while maintaining the same required signal-to-interference-plus-noise ratio.

Research on Constant Current Output Control of Wireless Power Transmission System Based on Parameter Identification

Research on Constant Current Output Control of Wireless Power Transmission System Based on Parameter Identification

Double helix rotating TENGs driven by ultra-low loading for harvesting high-entropy water flow energy

Water flow energy in rivers and lakes is a huge clean energy source and widely distributed in nature. Its low water velocity makes it difficult for electromagnetic generators to effectively harvest this energy. Therefore, harvesting high-entropy water flow energy is of great significance to the development of distributed power supply and sensing. In this study, we present a double helix rotating triboelectric nanogenerator (DHR-TENG) that can effectively convert the ultra-low water flow energy into electrical energy. DHR-TENG is designed to optimize space utilization and has a reciprocating transmission mechanism to ensure the continuous operation, achieving a surprisingly high charge density of 356.69 μC⋅m-3 and peak power density of 11.66 W⋅m-3. Compared with similar structures reported by predecessors, the maximum elevation is about 242 times and 2 times, respectively. A stable electrical output performance was achieved by DHR-TENG at ultra-low water flow velocity of 0.4∼0.8 m/s, the harvested energy can drive self-powered temperature sensor systems and wireless signal transmission systems. This work not only effectively improves the electrical output performance at ultra-low water flow velocity, but also helps to promote environmental monitoring and provides a feasible method for establishing early warning in the environment.

Triangulated Transmitter Coils With High Design Freedom for Free-Positioning and Omnidirectional Wireless Power Transfer System

Triangulated Transmitter Coils With High Design Freedom for Free-Positioning and Omnidirectional Wireless Power Transfer System

Additively manufactured conductive and dielectric 3D metasurfaces for independent manipulation of broadband orbital angular momentum

Recent developments in conductive and dielectric multi-material additive manufacturing have generated significant interest for their potential to enhance the performance of electronic devices. Lights-out digital manufacturing, a sophisticated multi-material printing technique, facilitates the seamless sintering of silver nanoparticles and acrylate inks, enabling the creation of functional electronic devices. In this study, the LDM process is utilized to prototype intricate 3D metasurface designs. A comprehensive analysis of the properties of the printed materials confirms the practicality of this multi-material printing approach. The research examines various element distributions within multi-metal layer structures, including carbon, phosphorus, oxygen, sulfur, and fluorine, to investigate the interfaces between conductive and dielectric structures. In addition, the conductivities of silver nanoparticle inks at different curing temperatures are studied to identify optimal printing conditions. Both the conductive and dielectric inks exhibit precisely controlled particle sizes and exceptional stability, which are critical for accurate additive manufacturing. Using this sophisticated printing solution, the paper presents a broadband 3D metasurface engineered to independently manipulate two distinct orbital angular momentum states in the V-band (60–70 GHz) and W-band (79–90 GHz), suitable for high-speed wireless communications. The integration of deep neural networks helps reduce the phase coupling between the frequency bands, enabling independent control of the OAM states. The structure of the dual-band metasurface features four distinct silver layers yet is fabricated on a single ultra-thin planar substrate, demonstrating its potential for applications in future wireless communication systems.