Nowadays,the rapid development of artificial intelligence(AI)provides a fresh perspective in designing future wireless communication systems.Innumerable attempts exploiting AI methods have been carried out,which resul...Nowadays,the rapid development of artificial intelligence(AI)provides a fresh perspective in designing future wireless communication systems.Innumerable attempts exploiting AI methods have been carried out,which results in the state-of-the-art performance in many different areas of wireless communications.In this article,we present the most recent and insightful developments that demonstrate the potentials of AI techniques in different physical layer(PHY)components and applications including channel characterization,channel coding,intelligent signal identification,channel estimation,new PHY for random access in massive machine-type communication(mMTC),massive multiple-input multiple-output(MIMO)power control and PHY resource management.Open challenges and potential future directions are identified and discussed along this research line.展开更多
Healthcare is one of the major applications of wireless systems that possess crucial issues. Specifically developing countries require a tow cost and reliable network with efficient protocols. The most challenging con...Healthcare is one of the major applications of wireless systems that possess crucial issues. Specifically developing countries require a tow cost and reliable network with efficient protocols. The most challenging concern of Body Area Network （BAN） is heterogeneity, which requires fairness with reliability among all the network nodes. Solutions proposed for these networks either do not provide fair packet transmission or consume high energy and introduce delays. In this paper, we propose a cross layer protocol for healthcare applications meeting the requirements and challenges of the heterogeneous BAN. The protocol is also feasible for developing countries as it can be implemented over existing wireless infrastructure and provides high network reliability with energy efficiency through cooperation and adaptability. Results show that the proposed scheme improves reliability, throughput, Packet Delivery Ratio （PDR）, and energy consumption for scalable and mobile networks over conventional BAN protocols.展开更多
Diadochokinesia pertains to a standard aspect of the conventional neurological examination, which involves the oscillation between muscle groups with an agonist and antagonist relationship. A representative example is...Diadochokinesia pertains to a standard aspect of the conventional neurological examination, which involves the oscillation between muscle groups with an agonist and antagonist relationship. A representative example is the pronation and supination of the forearm. Hemiparesis visibly demonstrates disparity of diadochokinesia, and clinical quantification is achieved through the use of an ordinal scale, which is inherently subjective. A conformal wearable and wireless inertial sensor equipped with a gyroscope mounted about the dorsum of the hand can objectively quantify diadochokinesia respective of forearm pronation and supination. The objective of the research endeavor was to apply an assortment of machine learning algorithms to distinguish between a hemiplegic affected and unaffected upper limb pair based on diadochokinesia with respect to pronation and supination of the forearm. Performance of the machine learning algorithms, such as the multilayer perceptron neural network, J48 decision tree, random forest, K-nearest neighbors, logistic regression, and naïve Bayes, were evaluated in consideration of classification accuracy and time to develop the machine learning model. The machine learning feature set was derived from the acquired gyroscope signal data. Using the gyroscope signal data from the conformal wearable and wireless inertial sensor the logistic regression and naïve Bayes machine learning algorithms achieved considerable performance capability with respect to both time to converge the machine learning model and classification accuracy for distinguishing between a hemiplegic upper limb pair for diadochokinesia in consideration of pronation and supination.展开更多
This article explores the design of a wireless fire alarm system supported by advanced data fusion technology.It includes discussions on the basic design ideas of the wireless fire alarm system,hardware design analysi...This article explores the design of a wireless fire alarm system supported by advanced data fusion technology.It includes discussions on the basic design ideas of the wireless fire alarm system,hardware design analysis,software design analysis,and simulation analysis,all supported by data fusion technology.Hopefully,this analysis can provide some reference for the rational application of data fusion technology to meet the actual design and application requirements of the system.展开更多
Recent advancements in passive wireless sensor technology have significantly extended the application scope of sensing,particularly in challenging environments for monitoring industry and healthcare applications.These...Recent advancements in passive wireless sensor technology have significantly extended the application scope of sensing,particularly in challenging environments for monitoring industry and healthcare applications.These systems are equipped with battery-free operation,wireless connectivity,and are designed to be both miniaturized and lightweight.Such features enable the safe,real-time monitoring of industrial environments and support high-precision physiological measurements in confined internal body spaces and on wearable epidermal devices.Despite the exploration into diverse application environments,the development of a systematic and comprehensive research framework for system architecture remains elusive,which hampers further optimization of these systems.This review,therefore,begins with an examination of application scenarios,progresses to evaluate current system architectures,and discusses the function of each component—specifically,the passive sensor module,the wireless communication model,and the readout module—within the context of key implementations in target sensing systems.Furthermore,we present case studies that demonstrate the feasibility of proposed classified components for sensing scenarios,derived from this systematic approach.By outlining a research trajectory for the application of passive wireless systems in sensing technologies,this paper aims to establish a foundation for more advanced,user-friendly applications.展开更多
Demand Side Management(DSM)is a vital issue in smart grids,given the time-varying user demand for electricity and power generation cost over a day.On the other hand,wireless communications with ubiquitous connectivity...Demand Side Management(DSM)is a vital issue in smart grids,given the time-varying user demand for electricity and power generation cost over a day.On the other hand,wireless communications with ubiquitous connectivity and low latency have emerged as a suitable option for smart grid.The design of any DSM system using a wireless network must consider the wireless link impairments,which is missing in existing literature.In this paper,we propose a DSM system using a Real-Time Pricing(RTP)mechanism and a wireless Neighborhood Area Network(NAN)with data transfer uncertainty.A Zigbee-based Internet of Things(IoT)model is considered for the communication infrastructure of the NAN.A sample NAN employing XBee and Raspberry Pi modules is also implemented in real-world settings to evaluate its reliability in transferring smart grid data over a wireless link.The proposed DSM system determines the optimal price corresponding to the optimum system welfare based on the two-way wireless communications among users,decision-makers,and energy providers.A novel cost function is adopted to reduce the impact of changes in user numbers on electricity prices.Simulation results indicate that the proposed system benefits users and energy providers.Furthermore,experimental results demonstrate that the success rate of data transfer significantly varies over the implemented wireless NAN,which can substantially impact the performance of the proposed DSM system.Further simulations are then carried out to quantify and analyze the impact of wireless communications on the electricity price,user welfare,and provider welfare.展开更多
This article discusses the detailed examination of the engineering design and implementation process for direct Train-to-Train(T2T)communication within a wireless train backbone network in the context of a virtual cou...This article discusses the detailed examination of the engineering design and implementation process for direct Train-to-Train(T2T)communication within a wireless train backbone network in the context of a virtual coupling scenario.The article proposed several critical aspects,including the optimization of transmission data requirements,which is essential to ensure that communication between trains is efficient and reliable.The design of the T2T wireless communication subsystem is discussed in detail,outlining the technical specifications,protocols,and technologies employed to facilitate wireless communication between multiple trains.Additionally,the article presents a thorough analysis of the data collected during real-world train experiments,highlighting the performance metrics and challenges encountered during testing.This empirical data not only validates the effectiveness of the proposed design but also serves as a crucial reference for future advancements in T2T wireless communication systems.By combining both theoretical principles and practical outcomes,the article offers insights that will aid engineers and researchers in developing robust and efficient wireless communication systems for next-generation train operations.展开更多
This paper designs a high-frequency stable wireless amplitude modulation(AM)system based on a Pierce circuit.The system utilizes an oscillator and comparator to generate a 20 kHz square wave with an adjustable duty cy...This paper designs a high-frequency stable wireless amplitude modulation(AM)system based on a Pierce circuit.The system utilizes an oscillator and comparator to generate a 20 kHz square wave with an adjustable duty cycle,combined with a 41 MHz carrier wave produced by a passive crystal oscillator Pierce circuit.A 100% modulation index amplitude modulation is achieved through the AD835 multiplier.The modulated signal is amplified by a power amplifier circuit and transmitted wirelessly via the transmitter antenna.Upon reception,the signal undergoes two-stage highfrequency amplification before passing through a Schottky diode envelope detector.The NE5532 shaping circuit then restores the square wave.Experimental results demonstrate reliable 11-meter transmission with carrier frequency deviation<0.75% and demodulation error<1%.展开更多
Single-signal detection in orthogonal frequency-divisionmultiplexing(OFDM)systems presents a challenge due to the time-varying nature of wireless channels.Although conventional methods have limitations,particularly in...Single-signal detection in orthogonal frequency-divisionmultiplexing(OFDM)systems presents a challenge due to the time-varying nature of wireless channels.Although conventional methods have limitations,particularly inmulti-inputmultioutput orthogonal frequency divisionmultiplexing(MIMO-OFDM)systems,this paper addresses this problem by exploring advanced deep learning approaches for combined channel estimation and signal detection.Specifically,we propose two hybrid architectures that integrate a convolutional neural network(CNN)with a recurrent neural network(RNN),namely,CNN-long short-term memory(CNN-LSTM)and CNN-bidirectional-LSTM(CNNBi-LSTM),designed to enhance signal detection performance in MIMO-OFDM systems.The proposed CNN-LSTM and CNN-Bi-LSTM architectures are evaluated and compared with both traditional methods and standalone deep learning models.Training was conducted offline using a dataset generated from a 2×2 MIMO-OFDM system with a 3GPP 5G channel model.The trained models are evaluated using accuracy,loss,and computational time,and further analysis of signal detection performance is based on bit error rate,optimal cyclic prefix length,and optimal pilot subcarrier configurations under various noise conditions and channel uncertainty scenarios.The results demonstrate that the proposed CNN-based architectures,particularly the CNN-Bi-LSTM trained model,significantly reduce the need for pilot and cyclic prefix symbols while delivering superior performance,especially at SNRs.All the hybrid deep learning architectures(CNN-LSTM,CNN-Bi-LSTM)demonstrated greater robustness and adaptability under dynamic channel conditions,outperforming conventional methods and benchmark deep learning architectures.These results indicate the effectiveness of CNN-based feature extractors in learning generalized spatial patterns,positioning these hybrid models as highly efficient and reliable solutions for MIMO-OFDM signal detection in 5G and future wireless communication systems.展开更多
Soft robots have partially or entirely provided versatile opportunities for issues or roles that cannot be addressed by conventional machine robots,although most studies are limited to designs,controls,or physical/mec...Soft robots have partially or entirely provided versatile opportunities for issues or roles that cannot be addressed by conventional machine robots,although most studies are limited to designs,controls,or physical/mechanical motions.Here,we present a transformable,reconfigurable robotic platform created by the integration of magnetically responsive soft composite matrices with deformable multifunctional electronics.Magnetic compounds engineered to undergo phase transition at a low temperature can readily achieve reversible magnetization and conduct various changes of motions and shapes.Thin and flexible electronic system designed with mechanical dynamics does not interfere with movements of the soft electronic robot,and the performances of wireless circuit,sensors,and devices are independent of a variety of activities,all of which are verified by theoretical studies.Demonstration of navigations and electronic operations in an artificial track highlights the potential of the integrated soft robot for on-demand,environments-responsive movements/metamorphoses,and optoelectrical detection and stimulation.Further improvements to a miniaturized,sophisticated system with material options enable in situ monitoring and treatment in envisioned areas such as biomedical implants.展开更多
This paper studies a cooperative relay transmission system within the framework of Multiple-Input Multiple-Output Radio Frequency/Underwater Optical Wireless Communication(MIMO-RF/UOWC),aiming to establish sea-based h...This paper studies a cooperative relay transmission system within the framework of Multiple-Input Multiple-Output Radio Frequency/Underwater Optical Wireless Communication(MIMO-RF/UOWC),aiming to establish sea-based heterogeneous networks.In this setup,the RF links obey κ-μ fading,while the UOWC links undergo the generalized Gamma fading with the pointing error impairments.The relay operates under an Amplify-and-Forward(AF)protocol.Additionally,the attenuation caused by the Absorption and Scattering(AaS)is considered in UOWC links.The work yields precise results for the Average Channel Capacity(ACC),Outage Probability(OP),and average Bit Error Rate(BER).Furthermore,to reveal deeper insights,bounds on the ACC and asymptotic results for the OP and average BER are derived.The findings highlight the superior performance of MIMO-RF/UOWC AF systems compared to Single-Input-Single-Output(SISO)-RF/UOWC AF systems.Various factors affecting the Diversity Gain(DG)of the MIMO-RF/UOWC AF system include the number of antennas/apertures,fading parameters of both links,and pointing error parameters.Moreover,while an increase in the AaS effect can result in significant attenuation,it does not determine the achievable DG of the proposed MIMO-RF/UOWC AF relaying system.展开更多
In indoor environments,various batterypowered Internet of Things(IoT)devices,such as remote controllers and electronic tags on high-level shelves,require efficient energy management.However,manually monitoring remaini...In indoor environments,various batterypowered Internet of Things(IoT)devices,such as remote controllers and electronic tags on high-level shelves,require efficient energy management.However,manually monitoring remaining energy levels and battery replacement is both inadequate and costly.This paper introduces an energy management system for indoor IoT,which includes a mobile energy station(ES)for enabling on-demand wireless energy transfer(WET)in radio frequency(RF),some energy receivers(ERs),and a cloud server.By implementing a two-stage positioning system and embedding energy receivers into traditional IoT devices,we robustly manage their energy storage.The experimental results demonstrate that the energy receiver can harvest a minimum power of 58 mW.展开更多
We demonstrate a 200 m outdoor 2×2 multiple-input multiple-output(MIMO)terahertz(THz)communication system operating at 300 GHz with 200 Gb/s polarization-division multiplexed quadrature phase-shift keying(PDM-QPS...We demonstrate a 200 m outdoor 2×2 multiple-input multiple-output(MIMO)terahertz(THz)communication system operating at 300 GHz with 200 Gb/s polarization-division multiplexed quadrature phase-shift keying(PDM-QPSK)transmission.We propose an iteratively pruned two-dimensional convolutional neural network(2D CNN)equalizer that adaptively captures polarization crosstalk and temporal nonlinearities through 2D convolution kernels.The system achieves a bit error rate(BER)below the hard-decision forward error correction(HD-FEC)threshold at a lower power of 6 d Bm,while reducing the computational complexity by 30.2%compared to the iteratively pruned one-dimensional(1D)CNN approach.This enables high-capacity and energy-efficient operation in long-distance THz links.展开更多
Space-Based Solar Power(SBSP) presents a promising solution for achieving carbon neutrality and Renewable Electricity 100%(RE100) goals by offering a stable and continuous energy supply. However, its commercialization...Space-Based Solar Power(SBSP) presents a promising solution for achieving carbon neutrality and Renewable Electricity 100%(RE100) goals by offering a stable and continuous energy supply. However, its commercialization faces significant obstacles due to the technical challenges of long-distance microwave Wireless Power Transmission(WPT) from geostationary orbit. Even ground-based kilometer-scale WPT experiments remain difficult because of limited testing infrastructure, high costs, and strict electromagnetic wave regulations. Since the 1975 NASA-Raytheon experiment, which successfully recovered 30 kW of power over 1.55 km, there has been little progress in extending the transmission distance or increasing the retrieved power. This study proposes a cost-effective methodology for conducting long-range WPT experiments in constrained environments by utilizing existing infrastructure. A deep space antenna operating at 2.08 GHz with an output power of 2.3 kW and a gain of 55.3 dBi was used as the transmitter. Two test configurations were implemented: a 1.81 km ground-to-air test using an aerostat to elevate the receiver and a 1.82 km ground-to-ground test using a ladder truck positioned on a plateau. The rectenna consists of a lightweight 3×3 patch antenna array(0.9 m × 0.9 m), accompanied by a steering device and LED indicators to verify power reception. The aerostat-based test achieved a power density of 154.6 mW/m2, which corresponds to approximately 6.2% of the theoretical maximum. The performance gap is primarily attributed to near-field interference, detuning of the patch antenna, rectifier mismatch, and alignment issues. These limitations are expected to be mitigated through improved patch antenna fabrication, a transition from GaN to GaAs rectifiers optimized for lower input power, and the implementation of an automated alignment system. With these enhancements, the recovered power is expected to improve by approximately four to five times. The results demonstrate a practical and scalable framework for long-range WPT experiments under constrained conditions and provide key insights for advancing SBSP technology.展开更多
Dear Editor,This letter presents a joint probabilistic scheduling and resource allocation method(PSRA) for 5G-based wireless networked control systems(WNCSs). As a control-aware optimization method, PSRA minimizes the...Dear Editor,This letter presents a joint probabilistic scheduling and resource allocation method(PSRA) for 5G-based wireless networked control systems(WNCSs). As a control-aware optimization method, PSRA minimizes the linear quadratic Gaussian(LQG) control cost of WNCSs by optimizing the activation probability of subsystems, the number of uplink repetitions, and the durations of uplink and downlink phases. Simulation results show that PSRA achieves smaller LQG control costs than existing works.展开更多
Founded in September 2020,the International SparkLink Alliance(iSLA)now has approximately 1,200 members in diverse sectors including terminals,homes,vehicles,manufacturing,transportation,finance and healthcare.The iSL...Founded in September 2020,the International SparkLink Alliance(iSLA)now has approximately 1,200 members in diverse sectors including terminals,homes,vehicles,manufacturing,transportation,finance and healthcare.The iSLA has established a technical standards system for wireless short-range communication covering full-stack standards such as the end-to-end protocol system.展开更多
Wi-Fi technology has evolved significantly since its introduction in 1997,advancing to Wi-Fi 6 as the latest standard,with Wi-Fi 7 currently under development.Despite these advancements,integrating machine learning in...Wi-Fi technology has evolved significantly since its introduction in 1997,advancing to Wi-Fi 6 as the latest standard,with Wi-Fi 7 currently under development.Despite these advancements,integrating machine learning into Wi-Fi networks remains challenging,especially in decentralized environments with multiple access points(mAPs).This paper is a short review that summarizes the potential applications of federated reinforcement learning(FRL)across eight key areas of Wi-Fi functionality,including channel access,link adaptation,beamforming,multi-user transmissions,channel bonding,multi-link operation,spatial reuse,and multi-basic servic set(multi-BSS)coordination.FRL is highlighted as a promising framework for enabling decentralized training and decision-making while preserving data privacy.To illustrate its role in practice,we present a case study on link activation in a multi-link operation(MLO)environment with multiple APs.Through theoretical discussion and simulation results,the study demonstrates how FRL can improve performance and reliability,paving the way for more adaptive and collaborative Wi-Fi networks in the era of Wi-Fi 7 and beyond.展开更多
Wireless Sensor Networks(WSNs)have become foundational in numerous real-world applications,ranging from environmental monitoring and industrial automation to healthcare systems and smart city development.As these netw...Wireless Sensor Networks(WSNs)have become foundational in numerous real-world applications,ranging from environmental monitoring and industrial automation to healthcare systems and smart city development.As these networks continue to grow in scale and complexity,the need for energy-efficient,scalable,and robust communication protocols becomes more critical than ever.Metaheuristic algorithms have shown significant promise in addressing these challenges,offering flexible and effective solutions for optimizing WSN performance.Among them,the Grey Wolf Optimizer(GWO)algorithm has attracted growing attention due to its simplicity,fast convergence,and strong global search capabilities.Accordingly,this survey provides an in-depth review of the applications of GWO and its variants for clustering,multi-hop routing,and hybrid cluster-based routing in WSNs.We categorize and analyze the existing GWO-based approaches across these key network optimization tasks,discussing the different problem formulations,decision variables,objective functions,and performance metrics used.In doing so,we examine standard GWO,multi-objective GWO,and hybrid GWO models that incorporate other computational intelligence techniques.Each method is evaluated based on how effectively it addresses the core constraints of WSNs,including energy consumption,communication overhead,and network lifetime.Finally,this survey outlines existing gaps in the literature and proposes potential future research directions aimed at enhancing the effectiveness and real-world applicability of GWO-based techniques for WSN clustering and routing.Our goal is to provide researchers and practitioners with a clear,structured understanding of the current state of GWO in WSNs and inspire further innovation in this evolving field.展开更多
In the wireless energy transmission service composition optimization problem,a key challenge is accurately capturing users’preferences for service criteria under complex influencing factors,and optimally selecting a ...In the wireless energy transmission service composition optimization problem,a key challenge is accurately capturing users’preferences for service criteria under complex influencing factors,and optimally selecting a composition solution under their budget constraints.Existing studies typically evaluate satisfaction solely based on energy transmission capacity,while overlooking critical factors such as price and trustworthiness of the provider,leading to a mismatch between optimization outcomes and user needs.To address this gap,we construct a user satisfaction evaluation model for multi-user and multi-provider scenarios,systematically incorporating service price,transmission capacity,and trustworthiness into the satisfaction assessment framework.Furthermore,we propose a Budget-Aware Preference Adjustment Model that predicts users’baseline preference weights from historical data and dynamically adjusts them according to budget levels,thereby reflecting user preferences more realistically under varying budget constraints.In addition,to tackle the composition optimization problem,we develop a ReflectiveEvolutionary Large Language Model—Guided Ant Colony Optimization algorithm,which leverages the reflective evolution capability of large language models to iteratively generate and refine heuristic information that guides the search process.Experimental results demonstrate that the proposed framework effectively integrates personalized preferences with budget sensitivity,accurately predicts users’preferences,and significantly enhances their satisfaction under complex constraints.展开更多
基金the Fundamental Research Funds for the Central Universities(2020JBM090,2020JBZD005)National Key R&D Program of China(2018YFE0207600,2020YFB1807201)+5 种基金the Key-Area Research and Development Program of Guangdong Province(2019B010157002)the Natural Science Foundation of China(61671046,61911530216,6196113039,U1834210)the Beijing Natural Science Foundation(L202019)the State Key Laboratory of Rail Traffic Control and Safety(RCS2021ZZ004,RCS2020ZT010)of Beijing Jiaotong UniversityNSFC Outstanding Youth Foundation under Grant 61725101the Royal Society Newton Advanced Fellowship under Grant NA191006.
文摘Nowadays,the rapid development of artificial intelligence(AI)provides a fresh perspective in designing future wireless communication systems.Innumerable attempts exploiting AI methods have been carried out,which results in the state-of-the-art performance in many different areas of wireless communications.In this article,we present the most recent and insightful developments that demonstrate the potentials of AI techniques in different physical layer(PHY)components and applications including channel characterization,channel coding,intelligent signal identification,channel estimation,new PHY for random access in massive machine-type communication(mMTC),massive multiple-input multiple-output(MIMO)power control and PHY resource management.Open challenges and potential future directions are identified and discussed along this research line.
文摘Healthcare is one of the major applications of wireless systems that possess crucial issues. Specifically developing countries require a tow cost and reliable network with efficient protocols. The most challenging concern of Body Area Network （BAN） is heterogeneity, which requires fairness with reliability among all the network nodes. Solutions proposed for these networks either do not provide fair packet transmission or consume high energy and introduce delays. In this paper, we propose a cross layer protocol for healthcare applications meeting the requirements and challenges of the heterogeneous BAN. The protocol is also feasible for developing countries as it can be implemented over existing wireless infrastructure and provides high network reliability with energy efficiency through cooperation and adaptability. Results show that the proposed scheme improves reliability, throughput, Packet Delivery Ratio （PDR）, and energy consumption for scalable and mobile networks over conventional BAN protocols.
文摘Diadochokinesia pertains to a standard aspect of the conventional neurological examination, which involves the oscillation between muscle groups with an agonist and antagonist relationship. A representative example is the pronation and supination of the forearm. Hemiparesis visibly demonstrates disparity of diadochokinesia, and clinical quantification is achieved through the use of an ordinal scale, which is inherently subjective. A conformal wearable and wireless inertial sensor equipped with a gyroscope mounted about the dorsum of the hand can objectively quantify diadochokinesia respective of forearm pronation and supination. The objective of the research endeavor was to apply an assortment of machine learning algorithms to distinguish between a hemiplegic affected and unaffected upper limb pair based on diadochokinesia with respect to pronation and supination of the forearm. Performance of the machine learning algorithms, such as the multilayer perceptron neural network, J48 decision tree, random forest, K-nearest neighbors, logistic regression, and naïve Bayes, were evaluated in consideration of classification accuracy and time to develop the machine learning model. The machine learning feature set was derived from the acquired gyroscope signal data. Using the gyroscope signal data from the conformal wearable and wireless inertial sensor the logistic regression and naïve Bayes machine learning algorithms achieved considerable performance capability with respect to both time to converge the machine learning model and classification accuracy for distinguishing between a hemiplegic upper limb pair for diadochokinesia in consideration of pronation and supination.
基金Chongqing Engineering University Undergraduate Innovation and Entrepreneurship Training Program Project:Wireless Fire Automatic Alarm System(Project No.:CXCY2024017)Chongqing Municipal Education Commission Science and Technology Research Project:Development and Research of Chongqing Wireless Fire Automatic Alarm System(Project No.:KJQN202401906)。
文摘This article explores the design of a wireless fire alarm system supported by advanced data fusion technology.It includes discussions on the basic design ideas of the wireless fire alarm system,hardware design analysis,software design analysis,and simulation analysis,all supported by data fusion technology.Hopefully,this analysis can provide some reference for the rational application of data fusion technology to meet the actual design and application requirements of the system.
基金partially supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(No.2018R1A6A1A03025242)by the Korea government(MIST)(RS-2023-00302751,RS-2024-00343686)the Research Grant of Kwangwoon University in 2024。
文摘Recent advancements in passive wireless sensor technology have significantly extended the application scope of sensing,particularly in challenging environments for monitoring industry and healthcare applications.These systems are equipped with battery-free operation,wireless connectivity,and are designed to be both miniaturized and lightweight.Such features enable the safe,real-time monitoring of industrial environments and support high-precision physiological measurements in confined internal body spaces and on wearable epidermal devices.Despite the exploration into diverse application environments,the development of a systematic and comprehensive research framework for system architecture remains elusive,which hampers further optimization of these systems.This review,therefore,begins with an examination of application scenarios,progresses to evaluate current system architectures,and discusses the function of each component—specifically,the passive sensor module,the wireless communication model,and the readout module—within the context of key implementations in target sensing systems.Furthermore,we present case studies that demonstrate the feasibility of proposed classified components for sensing scenarios,derived from this systematic approach.By outlining a research trajectory for the application of passive wireless systems in sensing technologies,this paper aims to establish a foundation for more advanced,user-friendly applications.
文摘Demand Side Management(DSM)is a vital issue in smart grids,given the time-varying user demand for electricity and power generation cost over a day.On the other hand,wireless communications with ubiquitous connectivity and low latency have emerged as a suitable option for smart grid.The design of any DSM system using a wireless network must consider the wireless link impairments,which is missing in existing literature.In this paper,we propose a DSM system using a Real-Time Pricing(RTP)mechanism and a wireless Neighborhood Area Network(NAN)with data transfer uncertainty.A Zigbee-based Internet of Things(IoT)model is considered for the communication infrastructure of the NAN.A sample NAN employing XBee and Raspberry Pi modules is also implemented in real-world settings to evaluate its reliability in transferring smart grid data over a wireless link.The proposed DSM system determines the optimal price corresponding to the optimum system welfare based on the two-way wireless communications among users,decision-makers,and energy providers.A novel cost function is adopted to reduce the impact of changes in user numbers on electricity prices.Simulation results indicate that the proposed system benefits users and energy providers.Furthermore,experimental results demonstrate that the success rate of data transfer significantly varies over the implemented wireless NAN,which can substantially impact the performance of the proposed DSM system.Further simulations are then carried out to quantify and analyze the impact of wireless communications on the electricity price,user welfare,and provider welfare.
基金supported by the National Key R&D Program of China(2021YFF0501103).
文摘This article discusses the detailed examination of the engineering design and implementation process for direct Train-to-Train(T2T)communication within a wireless train backbone network in the context of a virtual coupling scenario.The article proposed several critical aspects,including the optimization of transmission data requirements,which is essential to ensure that communication between trains is efficient and reliable.The design of the T2T wireless communication subsystem is discussed in detail,outlining the technical specifications,protocols,and technologies employed to facilitate wireless communication between multiple trains.Additionally,the article presents a thorough analysis of the data collected during real-world train experiments,highlighting the performance metrics and challenges encountered during testing.This empirical data not only validates the effectiveness of the proposed design but also serves as a crucial reference for future advancements in T2T wireless communication systems.By combining both theoretical principles and practical outcomes,the article offers insights that will aid engineers and researchers in developing robust and efficient wireless communication systems for next-generation train operations.
文摘This paper designs a high-frequency stable wireless amplitude modulation(AM)system based on a Pierce circuit.The system utilizes an oscillator and comparator to generate a 20 kHz square wave with an adjustable duty cycle,combined with a 41 MHz carrier wave produced by a passive crystal oscillator Pierce circuit.A 100% modulation index amplitude modulation is achieved through the AD835 multiplier.The modulated signal is amplified by a power amplifier circuit and transmitted wirelessly via the transmitter antenna.Upon reception,the signal undergoes two-stage highfrequency amplification before passing through a Schottky diode envelope detector.The NE5532 shaping circuit then restores the square wave.Experimental results demonstrate reliable 11-meter transmission with carrier frequency deviation<0.75% and demodulation error<1%.
基金supported by the IITP(Institute of Information&Communications Technology Planning&Evaluation)-ICAN(ICT Challenge and Advanced Network of HRD)grant funded by the Korea government(Ministry of Science and ICT)(IITP-2025-RS-2022-00156299).
文摘Single-signal detection in orthogonal frequency-divisionmultiplexing(OFDM)systems presents a challenge due to the time-varying nature of wireless channels.Although conventional methods have limitations,particularly inmulti-inputmultioutput orthogonal frequency divisionmultiplexing(MIMO-OFDM)systems,this paper addresses this problem by exploring advanced deep learning approaches for combined channel estimation and signal detection.Specifically,we propose two hybrid architectures that integrate a convolutional neural network(CNN)with a recurrent neural network(RNN),namely,CNN-long short-term memory(CNN-LSTM)and CNN-bidirectional-LSTM(CNNBi-LSTM),designed to enhance signal detection performance in MIMO-OFDM systems.The proposed CNN-LSTM and CNN-Bi-LSTM architectures are evaluated and compared with both traditional methods and standalone deep learning models.Training was conducted offline using a dataset generated from a 2×2 MIMO-OFDM system with a 3GPP 5G channel model.The trained models are evaluated using accuracy,loss,and computational time,and further analysis of signal detection performance is based on bit error rate,optimal cyclic prefix length,and optimal pilot subcarrier configurations under various noise conditions and channel uncertainty scenarios.The results demonstrate that the proposed CNN-based architectures,particularly the CNN-Bi-LSTM trained model,significantly reduce the need for pilot and cyclic prefix symbols while delivering superior performance,especially at SNRs.All the hybrid deep learning architectures(CNN-LSTM,CNN-Bi-LSTM)demonstrated greater robustness and adaptability under dynamic channel conditions,outperforming conventional methods and benchmark deep learning architectures.These results indicate the effectiveness of CNN-based feature extractors in learning generalized spatial patterns,positioning these hybrid models as highly efficient and reliable solutions for MIMO-OFDM signal detection in 5G and future wireless communication systems.
基金supported by the Korea Institute of Science and Technology(KIST)Institutional Program(Project No.2E32501-23-106)the National Research Foundation of Korea(NRF)grant funded by the Korea government(the Ministry of Science,ICT,MSIT)(RS-2022-00165524)+2 种基金the development of technologies for electroceuticals of National Research Foundation(NRF)funded by the Korean government(MSIT)(RS-2023-00220534)ICT Creative Consilience program through the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(IITP-2024-2020-0-01819)Start up Pioneering in Research and Innovation(SPRINT)through the Commercialization Promotion Agency for R&D Outcomes(COMPA)grant funded by the Korea government(Ministry of Science and ICT)(1711198921).
文摘Soft robots have partially or entirely provided versatile opportunities for issues or roles that cannot be addressed by conventional machine robots,although most studies are limited to designs,controls,or physical/mechanical motions.Here,we present a transformable,reconfigurable robotic platform created by the integration of magnetically responsive soft composite matrices with deformable multifunctional electronics.Magnetic compounds engineered to undergo phase transition at a low temperature can readily achieve reversible magnetization and conduct various changes of motions and shapes.Thin and flexible electronic system designed with mechanical dynamics does not interfere with movements of the soft electronic robot,and the performances of wireless circuit,sensors,and devices are independent of a variety of activities,all of which are verified by theoretical studies.Demonstration of navigations and electronic operations in an artificial track highlights the potential of the integrated soft robot for on-demand,environments-responsive movements/metamorphoses,and optoelectrical detection and stimulation.Further improvements to a miniaturized,sophisticated system with material options enable in situ monitoring and treatment in envisioned areas such as biomedical implants.
基金supported in part by the National Natural Science Foundation of China under Grant 62301272the Natural Science Research Start-up Foundation of Recruiting Talents of Nanjing University of Posts and Telecommunications under Grants NY223023 and NY223027.
文摘This paper studies a cooperative relay transmission system within the framework of Multiple-Input Multiple-Output Radio Frequency/Underwater Optical Wireless Communication(MIMO-RF/UOWC),aiming to establish sea-based heterogeneous networks.In this setup,the RF links obey κ-μ fading,while the UOWC links undergo the generalized Gamma fading with the pointing error impairments.The relay operates under an Amplify-and-Forward(AF)protocol.Additionally,the attenuation caused by the Absorption and Scattering(AaS)is considered in UOWC links.The work yields precise results for the Average Channel Capacity(ACC),Outage Probability(OP),and average Bit Error Rate(BER).Furthermore,to reveal deeper insights,bounds on the ACC and asymptotic results for the OP and average BER are derived.The findings highlight the superior performance of MIMO-RF/UOWC AF systems compared to Single-Input-Single-Output(SISO)-RF/UOWC AF systems.Various factors affecting the Diversity Gain(DG)of the MIMO-RF/UOWC AF system include the number of antennas/apertures,fading parameters of both links,and pointing error parameters.Moreover,while an increase in the AaS effect can result in significant attenuation,it does not determine the achievable DG of the proposed MIMO-RF/UOWC AF relaying system.
基金supported in part by the Natural Science Foundation of China(NSFC)under Grant 61971102in part by the Key Research and Development Program of Zhejiang Province under Grant 2022C01093.
文摘In indoor environments,various batterypowered Internet of Things(IoT)devices,such as remote controllers and electronic tags on high-level shelves,require efficient energy management.However,manually monitoring remaining energy levels and battery replacement is both inadequate and costly.This paper introduces an energy management system for indoor IoT,which includes a mobile energy station(ES)for enabling on-demand wireless energy transfer(WET)in radio frequency(RF),some energy receivers(ERs),and a cloud server.By implementing a two-stage positioning system and embedding energy receivers into traditional IoT devices,we robustly manage their energy storage.The experimental results demonstrate that the energy receiver can harvest a minimum power of 58 mW.
基金supported by the National Key R&D Program of China(No.2023YFB2905600)the National Natural Science Foundation of China(Nos.62127802,62331004,62305067,U24B20142,U24B20168,and 62427815)the Key Project of Jiangsu Province of China(No.BE2023001-4)。
文摘We demonstrate a 200 m outdoor 2×2 multiple-input multiple-output(MIMO)terahertz(THz)communication system operating at 300 GHz with 200 Gb/s polarization-division multiplexed quadrature phase-shift keying(PDM-QPSK)transmission.We propose an iteratively pruned two-dimensional convolutional neural network(2D CNN)equalizer that adaptively captures polarization crosstalk and temporal nonlinearities through 2D convolution kernels.The system achieves a bit error rate(BER)below the hard-decision forward error correction(HD-FEC)threshold at a lower power of 6 d Bm,while reducing the computational complexity by 30.2%compared to the iteratively pruned one-dimensional(1D)CNN approach.This enables high-capacity and energy-efficient operation in long-distance THz links.
文摘Space-Based Solar Power(SBSP) presents a promising solution for achieving carbon neutrality and Renewable Electricity 100%(RE100) goals by offering a stable and continuous energy supply. However, its commercialization faces significant obstacles due to the technical challenges of long-distance microwave Wireless Power Transmission(WPT) from geostationary orbit. Even ground-based kilometer-scale WPT experiments remain difficult because of limited testing infrastructure, high costs, and strict electromagnetic wave regulations. Since the 1975 NASA-Raytheon experiment, which successfully recovered 30 kW of power over 1.55 km, there has been little progress in extending the transmission distance or increasing the retrieved power. This study proposes a cost-effective methodology for conducting long-range WPT experiments in constrained environments by utilizing existing infrastructure. A deep space antenna operating at 2.08 GHz with an output power of 2.3 kW and a gain of 55.3 dBi was used as the transmitter. Two test configurations were implemented: a 1.81 km ground-to-air test using an aerostat to elevate the receiver and a 1.82 km ground-to-ground test using a ladder truck positioned on a plateau. The rectenna consists of a lightweight 3×3 patch antenna array(0.9 m × 0.9 m), accompanied by a steering device and LED indicators to verify power reception. The aerostat-based test achieved a power density of 154.6 mW/m2, which corresponds to approximately 6.2% of the theoretical maximum. The performance gap is primarily attributed to near-field interference, detuning of the patch antenna, rectifier mismatch, and alignment issues. These limitations are expected to be mitigated through improved patch antenna fabrication, a transition from GaN to GaAs rectifiers optimized for lower input power, and the implementation of an automated alignment system. With these enhancements, the recovered power is expected to improve by approximately four to five times. The results demonstrate a practical and scalable framework for long-range WPT experiments under constrained conditions and provide key insights for advancing SBSP technology.
基金supported by the Liaoning Revitalization Talents Program(XLYC2203148)
文摘Dear Editor,This letter presents a joint probabilistic scheduling and resource allocation method(PSRA) for 5G-based wireless networked control systems(WNCSs). As a control-aware optimization method, PSRA minimizes the linear quadratic Gaussian(LQG) control cost of WNCSs by optimizing the activation probability of subsystems, the number of uplink repetitions, and the durations of uplink and downlink phases. Simulation results show that PSRA achieves smaller LQG control costs than existing works.
文摘Founded in September 2020,the International SparkLink Alliance(iSLA)now has approximately 1,200 members in diverse sectors including terminals,homes,vehicles,manufacturing,transportation,finance and healthcare.The iSLA has established a technical standards system for wireless short-range communication covering full-stack standards such as the end-to-end protocol system.
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,Saudi Arabia,grant number RG-2-611-42(A.O.A.).
文摘Wi-Fi technology has evolved significantly since its introduction in 1997,advancing to Wi-Fi 6 as the latest standard,with Wi-Fi 7 currently under development.Despite these advancements,integrating machine learning into Wi-Fi networks remains challenging,especially in decentralized environments with multiple access points(mAPs).This paper is a short review that summarizes the potential applications of federated reinforcement learning(FRL)across eight key areas of Wi-Fi functionality,including channel access,link adaptation,beamforming,multi-user transmissions,channel bonding,multi-link operation,spatial reuse,and multi-basic servic set(multi-BSS)coordination.FRL is highlighted as a promising framework for enabling decentralized training and decision-making while preserving data privacy.To illustrate its role in practice,we present a case study on link activation in a multi-link operation(MLO)environment with multiple APs.Through theoretical discussion and simulation results,the study demonstrates how FRL can improve performance and reliability,paving the way for more adaptive and collaborative Wi-Fi networks in the era of Wi-Fi 7 and beyond.
文摘Wireless Sensor Networks(WSNs)have become foundational in numerous real-world applications,ranging from environmental monitoring and industrial automation to healthcare systems and smart city development.As these networks continue to grow in scale and complexity,the need for energy-efficient,scalable,and robust communication protocols becomes more critical than ever.Metaheuristic algorithms have shown significant promise in addressing these challenges,offering flexible and effective solutions for optimizing WSN performance.Among them,the Grey Wolf Optimizer(GWO)algorithm has attracted growing attention due to its simplicity,fast convergence,and strong global search capabilities.Accordingly,this survey provides an in-depth review of the applications of GWO and its variants for clustering,multi-hop routing,and hybrid cluster-based routing in WSNs.We categorize and analyze the existing GWO-based approaches across these key network optimization tasks,discussing the different problem formulations,decision variables,objective functions,and performance metrics used.In doing so,we examine standard GWO,multi-objective GWO,and hybrid GWO models that incorporate other computational intelligence techniques.Each method is evaluated based on how effectively it addresses the core constraints of WSNs,including energy consumption,communication overhead,and network lifetime.Finally,this survey outlines existing gaps in the literature and proposes potential future research directions aimed at enhancing the effectiveness and real-world applicability of GWO-based techniques for WSN clustering and routing.Our goal is to provide researchers and practitioners with a clear,structured understanding of the current state of GWO in WSNs and inspire further innovation in this evolving field.
基金supported by the National Natural Science Foundation of China under Grant 62472264the Natural Science Distinguished Youth Foundation of Shandong Province under Grant ZR2025QA13。
文摘In the wireless energy transmission service composition optimization problem,a key challenge is accurately capturing users’preferences for service criteria under complex influencing factors,and optimally selecting a composition solution under their budget constraints.Existing studies typically evaluate satisfaction solely based on energy transmission capacity,while overlooking critical factors such as price and trustworthiness of the provider,leading to a mismatch between optimization outcomes and user needs.To address this gap,we construct a user satisfaction evaluation model for multi-user and multi-provider scenarios,systematically incorporating service price,transmission capacity,and trustworthiness into the satisfaction assessment framework.Furthermore,we propose a Budget-Aware Preference Adjustment Model that predicts users’baseline preference weights from historical data and dynamically adjusts them according to budget levels,thereby reflecting user preferences more realistically under varying budget constraints.In addition,to tackle the composition optimization problem,we develop a ReflectiveEvolutionary Large Language Model—Guided Ant Colony Optimization algorithm,which leverages the reflective evolution capability of large language models to iteratively generate and refine heuristic information that guides the search process.Experimental results demonstrate that the proposed framework effectively integrates personalized preferences with budget sensitivity,accurately predicts users’preferences,and significantly enhances their satisfaction under complex constraints.
