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
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
Gel-based flexible wearable sensors have attracted considerable interest in aquatic environments.However,the development of underwater conductive gel sensors with outstanding anti-swelling,mechanical,and sensing capab...Gel-based flexible wearable sensors have attracted considerable interest in aquatic environments.However,the development of underwater conductive gel sensors with outstanding anti-swelling,mechanical,and sensing capabilities faces significant challenges.The aim of this study is to develop anti-swelling and conductive zwitterionic gels and investigate their applications in wireless underwater strain sensing.Multi-functional zwitterionic gels were fabricated by copolymerizing[2-(methacryloyloxy)ethyl]dimethyl-(3-sulfopropyl)ammonium hydroxide(SBMA)and acrylic acid(AA)in a mixed solution of aluminum chloride(AlCl3)and poly(vinyl alcohol)(PVA)under ultraviolet light(360 nm).PSBMA was switched from a neutral polymer to a positively charged polymer because of the combination of Al^(3+)with the negative groups SO_(3)^(−).The water molecules were eliminated because of electrostatic repulsion.The gels exhibited anti-swelling properties(swelling ratio<11%),high stretchability(600%strain),and toughness(2451 kJ/m^(3)).The PPAS-Al^(3+)gel was integrated with a wireless Bluetooth system to construct underwater wearable strain sensors that could accurately capture the signals caused by human joint movements and speech recognition even in water.Antibacterial activity(>98.9%inhibition)and stable wireless sensing have potential applications in the fields of wearable sensors,underwater communication,and intelligent healthcare.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 this paper,we propose a novel graph signal processing convolution recurrent network(GSP CRN)for signal enhancement against high suppressive interference(HSI)in wireless communications.GSPCRN consists of the short-t...In this paper,we propose a novel graph signal processing convolution recurrent network(GSP CRN)for signal enhancement against high suppressive interference(HSI)in wireless communications.GSPCRN consists of the short-time graph signal processing(SGSP)approach and a modified convolution recurrent network.Similar to the traditional shorttime time-frequency transformation,SGSP frames the complex-valued communication signal and transforms it to the graph-domain representations,where the connection and weight flexibility of each vertex are fully taken into account.In the presence of HSI,SGSP can extract signal features from new graph-domain dimensions and empower neural networks for weak signal enhancement.Two SGSP methods,adjacency singular value decomposition and implicit graph transformation,are designed to capture relationships among the sampling points in the segmented signals.Simulation results demonstrate that our proposed GSPCRN outperforms existing classic methods in extracting weak signals from the HSI environment.When the interference-to-signal ratio exceeds 27dB,only our proposed GSPCRN can achieve the interference mitigation.展开更多
In wireless sensor networks,ensuring communication security via specific emitter identification(SEI)is crucial.However,existing SEI methods are limited to closed-set scenarios and lack the ability to detect unknown de...In wireless sensor networks,ensuring communication security via specific emitter identification(SEI)is crucial.However,existing SEI methods are limited to closed-set scenarios and lack the ability to detect unknown devices and perform classincremental training.This study proposes a class-incremental open-set SEI approach.The open-set SEI model calculates radiofrequency fingerprints(RFFs)prototypes for known signals and employs a self-attention mechanism to enhance their discriminability.Detection thresholds are set through Gaussian fitting for each class.For class-incremental learning,the algorithm freezes the parameters of the previously trained model to initialize the new model.It designs specific losses:the RFFs extraction distribution difference loss and the prototype transformation distribution difference loss,which force the new model to retain old knowledge while learning new knowledge.The training loss enables learning of new class RFFs.Experimental results demonstrate that the open-set SEI model achieves state-of-theart performance and strong noise robustness.Moreover,the class-incremental learning algorithm effectively enables the model to retain old device RFFs knowledge,acquire new device RFFs knowledge,and detect unknown devices simultaneously.展开更多
The integration of High-Altitude Platform Stations(HAPS)with Reconfigurable Intelligent Surfaces(RIS)represents a critical advancement for next-generation wireless networks,offering unprecedented opportunities for ubi...The integration of High-Altitude Platform Stations(HAPS)with Reconfigurable Intelligent Surfaces(RIS)represents a critical advancement for next-generation wireless networks,offering unprecedented opportunities for ubiquitous connectivity.However,existing research reveals significant gaps in dynamic resource allocation,joint optimization,and equitable service provisioning under varying channel conditions,limiting practical deployment of these technologies.This paper addresses these challenges by proposing a novel Fairness-Aware Deep Q-Learning(FAIRDQL)framework for joint resource management and phase configuration in HAPS-RIS systems.Our methodology employs a comprehensive three-tier algorithmic architecture integrating adaptive power control,priority-based user scheduling,and dynamic learning mechanisms.The FAIR-DQL approach utilizes advanced reinforcement learning with experience replay and fairness-aware reward functions to balance competing objectives while adapting to dynamic environments.Key findings demonstrate substantial improvements:9.15 dB SINR gain,12.5 bps/Hz capacity,78%power efficiency,and 0.82 fairness index.The framework achieves rapid 40-episode convergence with consistent delay performance.These contributions establish new benchmarks for fairness-aware resource allocation in aerial communications,enabling practical HAPS-RIS deployments in rural connectivity,emergency communications,and urban networks.展开更多
[Objective]As wireless power transfer(WPT)technology is increasingly deployed in scenarios such as electric vehicles,metallic foreign objects in the WPT area may cause local overheating and energy loss.Existing method...[Objective]As wireless power transfer(WPT)technology is increasingly deployed in scenarios such as electric vehicles,metallic foreign objects in the WPT area may cause local overheating and energy loss.Existing methods still suffer from poor edge/corner sensitivity,misjudgment due to fixed thresholds,and limited ability to extract position information.This work proposes a wireless power transfer-foreign object detection(WPT-FOD)method based on channel differential response and a dynamic-threshold corner-enhancement strategy,aiming to improve detection sensitivity,localization accuracy,and robustness without altering the overall coil layout.[Method]A multi-channel detection coil array is designed,and the self-inductance disturbance response of each channel coil is modeled.A channel-difference mapping mechanism is introduced to build a 2-D sensitivity matrix to characterize spatial position correlation.A corner-enhancement algorithm is developed to weight and amplify the collaborative response of adjacent channels,and a dynamic threshold adjustment mechanism is integrated to adapt to varying interference levels.Validation is carried out on a self-built 64-channel FOD platform by moving a typical metallic foreign object across central,edge,and corner regions,and by conducting comparative tests under different interference intensities.[Result]When a typical metallic foreign object moves to corner regions,the self-inductance disturbance of the detection coil increases from less than 0.02μH to more than 0.06μH,significantly enhancing the discrimination capability at corners.Under varying interference strengths,the dynamic threshold mechanism reduces the number of false positives from 13 to 2,demonstrating good environmental adaptability and stability.[Conclusion]By combining channel differential response,corner enhancement,and dynamic thresholding,the proposed WPT-FOD effectively mitigates edge/corner blind spots and fixed-threshold misjudgment,while providing localization capability and robustness.It markedly improves the accuracy of metallic foreign object detection in WPT systems and offers a feasible path and method reference for the safe application and engineering deployment of WPT systems.展开更多
Artificial Intelligence(AI)-native sixth-generation(6G)wireless networks require data-efficient and channel-resilient physical-layer modeling techniques that learn stable device-specific representations under channel ...Artificial Intelligence(AI)-native sixth-generation(6G)wireless networks require data-efficient and channel-resilient physical-layer modeling techniques that learn stable device-specific representations under channel variations and hardware imperfections to support secure and reliable device-level authentication under highly dynamic environments.In such networks,massive device heterogeneity and time-varying channel conditions pose significant challenges,as reliable authentication must be achieved with limited labeled data and constrained edge resources.To address this challenge,this paper proposes an Artificial Intelligence(AI)-assisted few-shot physical-layer modeling framework for channel robust device identification,formulated within the paradigm of Specific Emitter Identification(SEI)based on radio frequency(RF)fingerprinting.The proposed framework explicitly formulates few-shot SEI as a channel-resilient physical-layer modeling problem by integrating a lightweight convolutional neural network and Transformer hybrid encoder with a dual-branch feature decoupling mechanism.Device specific RF fingerprints are separated from channel-dependent factors through orthogonality-constrained learning,which effectively suppresses channel-induced prototype drift and stabilizes metric geometry under channel variations.A meta-learned prototypical inference module is further employed under episodic few-shot training,enabling rapid adaptation to new devices and unseen channel conditions using only a small number of labeled samples.Experimental results on multiple realworld RF datasets,including ORACLE Wi-Fi transmitter measurements and civil aviation ADS-B broadcasts(DWi-Fi,DADS-B,and DDF17 ADS-B),demonstrate that the proposed method achieves identification accuracy ranging from 99.1%to 99.8%using only 10 labeled samples per device,while maintaining episode-level performance variance below 0.02.In addition,the proposed model contains approximately 1.45×10^(5) trainable parameters,making it suitable for deployment on resource-constrained edge devices.These results indicate that the proposed framework provides a concrete and scalable AI-driven solution for physical-layer security and device-level authentication in AI-native 6G wireless networks.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
In Wireless Sensor Networks(WSNs),survivability is a crucial issue that is greatly impacted by energy efficiency.Solutions that satisfy application objectives while extending network life are needed to address severe ...In Wireless Sensor Networks(WSNs),survivability is a crucial issue that is greatly impacted by energy efficiency.Solutions that satisfy application objectives while extending network life are needed to address severe energy constraints inWSNs.This paper presents an Adaptive Enhanced GreyWolf Optimizer(AEGWO)for energy-efficient cluster head(CH)selection that mitigates the exploration–exploitation imbalance,preserves population diversity,and avoids premature convergence inherent in baseline GWO.The AEGWO combines adaptive control of the parameter of the search pressure to accelerate convergence without stagnation,a hybrid velocity-momentum update based on the dynamics of PSO,and an intelligent mutation operator to maintain the diversity of the population.The search is guided by a multi-objective fitness,which aims at maximizing the residual energy,equal distribution of CH,minimizing the intra-cluster distance,desirable proximity to sinks,and enhancing the coverage.Simulations on 100 nodes homogeneousWSN Tested the proposed AEGWO under the same conditions with LEACH,GWO,IGWO,PSO,WOA,and GA,AEGWO significantly increases stability and lifetime compared to LEACHand other tested algorithms;it has the best first,half,and last node dead,and higher residual energy and smaller communication overhead.The findings prove that AEGWO provides sustainable energy management and better lifetime extension,which makes it a robust,flexible clustering protocol of large-scaleWSNs.展开更多
To tackle the physical layer security challenges in wireless communication,this paper introduces a multiuser architecture that leverages single-channel blind source separation,centered around a Multi-source Signal Mix...To tackle the physical layer security challenges in wireless communication,this paper introduces a multiuser architecture that leverages single-channel blind source separation,centered around a Multi-source Signal Mixture Separator(MSMS).This architecture consists of a multi-user encoder,a channel layer,and a separation decoder,allowing it to handle multiple functions simultaneously,including encoding,modulation,signal separation,demodulation,and decoding.The MSMS receiver effectively enables the separation of numerous user signals,making it exceedingly difficult for unauthorized eavesdroppers to extract valuable information from the mixed signals,thus significantly enhancing communication security.The MSMS can address the challenges of few-shot sample training and achieve joint optimization during transmission by employing a deep learning-based network design.The design of a single receiver reduces system costs and improves spectrum efficiency.The MSMS outperforms traditional Space-time Block Coding(STBC)strategies regarding separation performance,particularly in Block Error Rate(BLER)metrics.Modulation constellation diagrams further analyze the effectiveness of multi-source signal mixture separation.Moreover,this study extends the MSMS framework from a two-user scenario to a three-user scenario,further demonstrating the flexibility and scalability of the proposed architecture.展开更多
This paper studies wireless vehicular communication(VehCom)in intelligent transportation systems using an orthogonal frequency division multiplexing with index modulation(OFDM-IM).In the concept of IM,data is transmit...This paper studies wireless vehicular communication(VehCom)in intelligent transportation systems using an orthogonal frequency division multiplexing with index modulation(OFDM-IM).In the concept of IM,data is transmitted not only through the modulated symbols but also via the indices of the active subcarriers.In contrast to the original OFDM,OFDM-IM activates only non-zero subcarriers,increasing energy efficiency.However,the pilotassisted channel estimation(CE)method is a significant challenge in OFDM-IM,where the desired pilot subcarrier interval is related to the OFDM-IM subblock length.This paper proposes a walsh-scattered pilot-assisted CE for OFDM-IM VehCom.The optimum walsh-scattered pilot assignment is proposed to improve the transmission efficiency.Furthermore,a space-time block code with a high transmit diversity gain is employed for OFDM-IM VehCom to enhance VehCom's signal quality.The results show that the proposed method performs higher CE accuracy and better bit-error rate with significant spectral and energy efficiencies than conventional methods.展开更多
The relentless evolution of Information and Communication Technology(ICT) stands as a testament to the synergistic power of collaboration. It thrives at the dynamic intersection of industrial insight, academic rigor, ...The relentless evolution of Information and Communication Technology(ICT) stands as a testament to the synergistic power of collaboration. It thrives at the dynamic intersection of industrial insight, academic rigor, and dedicated research. This special issue, “Achievements of ZTE's Industry-University-Institute Cooperation Projects,” presents a curated collection of cutting-edge research that embodies the fruitful outcomes of deep collaboration, addressing some of the most pressing challenges across wireless communications, artificial intelligence(AI), software engineering, and industrial digitization.展开更多
This paper presents an image encryption scheme for underwater optical wireless communication(UOWC)systems based on dynamically generated hyperchaotic S-boxes,aiming to enhance both data security and transmission perfo...This paper presents an image encryption scheme for underwater optical wireless communication(UOWC)systems based on dynamically generated hyperchaotic S-boxes,aiming to enhance both data security and transmission performance in underwater environments.The proposed encryption approach provides strong confusion and diffusion properties and is evaluated over five Jerlov water types with different optical attenuation characteristics.Security analysis demonstrates that the encrypted images achieve information entropy values close to the ideal value of 8(7.9925–7.9993),with very low correlation coefficients in horizontal,vertical,and diagonal directions,as well as the system achieves high values in key metrics such as the Unified Average Changing Intensity(UACI)and Number of Pixel Change Rate(NPCR),ranging from 33.42 to 33.47 and from 99.58%to 99.62%,respectively,both near their theoretical optima.In addition to improving confidentiality,the hyperchaotic encryption process decorrelates pixel intensities and redistributes image spectral content,which enhances robustness against underwater absorption and scattering effects.As a result,improved transmission performance is observed;for example,in Jerlov type I(JI)water,the effective transmission distance is extended from16mfor plain images to 24mfor encrypted images,while the Peak Signal to Noise Ratio(PSNR)at 24 m increases from 9.25 to 20.13 dB after decryption and enhancement.These results confirmthat the proposed scheme provides a dual benefit of secure and reliable image transmission in UOWC systems.展开更多
The rapid growth of mobile and Internet of Things(IoT)applications in dense urban environments places stringent demands on future Beyond 5G(B5G)or Beyond 6G(B6G)networks,which must ensure high Quality of Service(QoS)w...The rapid growth of mobile and Internet of Things(IoT)applications in dense urban environments places stringent demands on future Beyond 5G(B5G)or Beyond 6G(B6G)networks,which must ensure high Quality of Service(QoS)while maintaining cost-efficiency and sustainable deployment.Traditional strategies struggle with complex 3D propagation,building penetration loss,and the balance between coverage and infrastructure cost.To address this challenge,this study presents the first application of a Global-best Guided Quantum-inspired Tabu Search with Quantum-Not Gate(GQTS-QNG)framework for 3D base-station deployment optimization.The problem is formulated as a multi-objective model that simultaneously maximizes coverage and minimizes deployment cost.A binary-to-decimal encodingmechanism is designed to represent discrete placement coordinates and base station types,leveraging a quantum-inspired method to efficiently search and refine solutions within challenging combinatorial environments.Global-best guidance and tabu memory are integrated to strengthen convergence stability and avoid revisiting previously explored solutions.Simulation results across user densities ranging from 1000 to 10,000 show that GQTS-QNG consistently finds deployment configurations achieving full coverage while reducing deployment cost compared with the state-of-the-art algorithms under equal iteration times.Additionally,our method generates welldistributed and structured Pareto fronts,offering diverse planning options that allow operators to flexibly balance cost and performance requirements.These findings demonstrate that GQTS-QNG is a scalable and efficient algorithm for sustainable 3D cellular network deployment in B5G/6G urban scenarios.展开更多
The integration of the intelligent reflecting surface(IRS)with simultaneous wireless information and power transfer(SWIPT)has emerged as a cost-effective and efficient solution to enhance the performance of informatio...The integration of the intelligent reflecting surface(IRS)with simultaneous wireless information and power transfer(SWIPT)has emerged as a cost-effective and efficient solution to enhance the performance of information and energy transfer.In this research,a hybrid active/passive IRS-assisted SWIPT system is proposed.Specifically,an active IRS(AIRS)and a passive IRS(PIRS)are deployed in the SWIPT system to facilitate a multiantenna base station(BS)in simultaneously delivering information and energy to multiple information users(IUs)and energy users(EUs).The objective is to maximize the sum throughput by jointly optimizing the transmitter beamforming and the reflection coefficient matrices of the AIRS and the PIRS while satisfying the transmitter power constraints,the energy harvesting(EH)requirements of EUs,and the AIRS amplification power limitations.However,the optimization variables are highly coupled and cannot be solved directly.To tackle this complex problem,we propose an efficient algorithm based on alternating optimization(AO)and semi-definite relaxation(SDR)techniques to obtain high-quality solutions.Simulation results demonstrate that the hybrid active/passive IRSassisted SWIPT system significantly enhances throughput performance and outperforms benchmark systems.展开更多
The wireless cloud robotic system(WCRS),which fully integrates sensing,communication,computing,and control capabilities as an intelligent agent,is a promising way to achieve intelligent manufacturing due to easy deplo...The wireless cloud robotic system(WCRS),which fully integrates sensing,communication,computing,and control capabilities as an intelligent agent,is a promising way to achieve intelligent manufacturing due to easy deployment and flexible expansion.However,the high-precision control of WCRS requires deterministic wireless communication,which is always challenging in the complex and dynamic radio space.This paper employs the reconfigurable intelligent surface(RIS)to establish a novel RIS-assisted WCRS architecture,where the radio channel is controlled to achieve ultra-reliable,low-delay,and low-jitter communication for high-precision closed-loop motion control.However,control and communication are strongly coupled and should be co-optimized.Fully considering the constraints of control input threshold,control delay deadline,beam phase,antenna power,and information distortion,we establish a stability maximization problem to jointly optimize control input compensation,RIS phase shift,and beamforming.Herein,a new jitter-oriented system stability objective with respect to control error and communication jitter is defined and the closed-form expression of control delay deadline is derived based on the Jensen Inequality and Lyapunov-Krasovskii functional.Due to the time-varying and partial observability of the channel and robot states,we model the problem as a partially observable Markov decision process(POMDP).To solve this complex problem,we propose a multi-agent transfer reinforcement learning algorithm named LSTM-PPO-MATRL,where the LSTM-enhanced proximal policy optimization(PPO)is designed to approximate an optimal solution and the option-guided policy transfer learning is proposed to facilitate the learning process.By centralized training and decentralized execution,LSTM-PPO-MATRL is validated by extensive experiments on MuJoCo tasks for both low-mobility and high-mobility robotic control scenarios.The results demonstrate that LSTM-PPO-MATRL not only realizes high learning efficiency,but also supports low-delay,low-jitter communication for low error control,where 71.9%control accuracy improvement and 68.7%delay jitter reduction are achieved compared to the PPO-MADRL baseline.展开更多
The complex wiring,bulky data collection devices,and difficulty in fast and on-site data interpretation significantly limit the practical application of flexible strain sensors as wearable devices.To tackle these chal...The complex wiring,bulky data collection devices,and difficulty in fast and on-site data interpretation significantly limit the practical application of flexible strain sensors as wearable devices.To tackle these challenges,this work develops an artificial intelligenceassisted,wireless,flexible,and wearable mechanoluminescent strain sensor system(AIFWMLS)by integration of deep learning neural network-based color data processing system(CDPS)with a sandwich-structured flexible mechanoluminescent sensor(SFLC)film.The SFLC film shows remarkable and robust mechanoluminescent performance with a simple structure for easy fabrication.The CDPS system can rapidly and accurately extract and interpret the color of the SFLC film to strain values with auto-correction of errors caused by the varying color temperature,which significantly improves the accuracy of the predicted strain.A smart glove mechanoluminescent sensor system demonstrates the great potential of the AIFWMLS system in human gesture recognition.Moreover,the versatile SFLC film can also serve as a encryption device.The integration of deep learning neural network-based artificial intelligence and SFLC film provides a promising strategy to break the“color to strain value”bottleneck that hinders the practical application of flexible colorimetric strain sensors,which could promote the development of wearable and flexible strain sensors from laboratory research to consumer markets.展开更多
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.
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financially supported by the Fundamental Research Program of the Shanxi Province(No.202203021211023).
文摘Gel-based flexible wearable sensors have attracted considerable interest in aquatic environments.However,the development of underwater conductive gel sensors with outstanding anti-swelling,mechanical,and sensing capabilities faces significant challenges.The aim of this study is to develop anti-swelling and conductive zwitterionic gels and investigate their applications in wireless underwater strain sensing.Multi-functional zwitterionic gels were fabricated by copolymerizing[2-(methacryloyloxy)ethyl]dimethyl-(3-sulfopropyl)ammonium hydroxide(SBMA)and acrylic acid(AA)in a mixed solution of aluminum chloride(AlCl3)and poly(vinyl alcohol)(PVA)under ultraviolet light(360 nm).PSBMA was switched from a neutral polymer to a positively charged polymer because of the combination of Al^(3+)with the negative groups SO_(3)^(−).The water molecules were eliminated because of electrostatic repulsion.The gels exhibited anti-swelling properties(swelling ratio<11%),high stretchability(600%strain),and toughness(2451 kJ/m^(3)).The PPAS-Al^(3+)gel was integrated with a wireless Bluetooth system to construct underwater wearable strain sensors that could accurately capture the signals caused by human joint movements and speech recognition even in water.Antibacterial activity(>98.9%inhibition)and stable wireless sensing have potential applications in the fields of wearable sensors,underwater communication,and intelligent healthcare.
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 he National Social Science Found of China(2022-SKJJ-B-112).
文摘In this paper,we propose a novel graph signal processing convolution recurrent network(GSP CRN)for signal enhancement against high suppressive interference(HSI)in wireless communications.GSPCRN consists of the short-time graph signal processing(SGSP)approach and a modified convolution recurrent network.Similar to the traditional shorttime time-frequency transformation,SGSP frames the complex-valued communication signal and transforms it to the graph-domain representations,where the connection and weight flexibility of each vertex are fully taken into account.In the presence of HSI,SGSP can extract signal features from new graph-domain dimensions and empower neural networks for weak signal enhancement.Two SGSP methods,adjacency singular value decomposition and implicit graph transformation,are designed to capture relationships among the sampling points in the segmented signals.Simulation results demonstrate that our proposed GSPCRN outperforms existing classic methods in extracting weak signals from the HSI environment.When the interference-to-signal ratio exceeds 27dB,only our proposed GSPCRN can achieve the interference mitigation.
基金supported by the National Natural Science Foundation of China(62371465)Taishan Scholar Project of Shandong Province(ts201511020)。
文摘In wireless sensor networks,ensuring communication security via specific emitter identification(SEI)is crucial.However,existing SEI methods are limited to closed-set scenarios and lack the ability to detect unknown devices and perform classincremental training.This study proposes a class-incremental open-set SEI approach.The open-set SEI model calculates radiofrequency fingerprints(RFFs)prototypes for known signals and employs a self-attention mechanism to enhance their discriminability.Detection thresholds are set through Gaussian fitting for each class.For class-incremental learning,the algorithm freezes the parameters of the previously trained model to initialize the new model.It designs specific losses:the RFFs extraction distribution difference loss and the prototype transformation distribution difference loss,which force the new model to retain old knowledge while learning new knowledge.The training loss enables learning of new class RFFs.Experimental results demonstrate that the open-set SEI model achieves state-of-theart performance and strong noise robustness.Moreover,the class-incremental learning algorithm effectively enables the model to retain old device RFFs knowledge,acquire new device RFFs knowledge,and detect unknown devices simultaneously.
基金supported by the Princess Nourah bint Abdulrahman University Researchers Supporting Project,number PNURSP2025R757Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The integration of High-Altitude Platform Stations(HAPS)with Reconfigurable Intelligent Surfaces(RIS)represents a critical advancement for next-generation wireless networks,offering unprecedented opportunities for ubiquitous connectivity.However,existing research reveals significant gaps in dynamic resource allocation,joint optimization,and equitable service provisioning under varying channel conditions,limiting practical deployment of these technologies.This paper addresses these challenges by proposing a novel Fairness-Aware Deep Q-Learning(FAIRDQL)framework for joint resource management and phase configuration in HAPS-RIS systems.Our methodology employs a comprehensive three-tier algorithmic architecture integrating adaptive power control,priority-based user scheduling,and dynamic learning mechanisms.The FAIR-DQL approach utilizes advanced reinforcement learning with experience replay and fairness-aware reward functions to balance competing objectives while adapting to dynamic environments.Key findings demonstrate substantial improvements:9.15 dB SINR gain,12.5 bps/Hz capacity,78%power efficiency,and 0.82 fairness index.The framework achieves rapid 40-episode convergence with consistent delay performance.These contributions establish new benchmarks for fairness-aware resource allocation in aerial communications,enabling practical HAPS-RIS deployments in rural connectivity,emergency communications,and urban networks.
文摘[Objective]As wireless power transfer(WPT)technology is increasingly deployed in scenarios such as electric vehicles,metallic foreign objects in the WPT area may cause local overheating and energy loss.Existing methods still suffer from poor edge/corner sensitivity,misjudgment due to fixed thresholds,and limited ability to extract position information.This work proposes a wireless power transfer-foreign object detection(WPT-FOD)method based on channel differential response and a dynamic-threshold corner-enhancement strategy,aiming to improve detection sensitivity,localization accuracy,and robustness without altering the overall coil layout.[Method]A multi-channel detection coil array is designed,and the self-inductance disturbance response of each channel coil is modeled.A channel-difference mapping mechanism is introduced to build a 2-D sensitivity matrix to characterize spatial position correlation.A corner-enhancement algorithm is developed to weight and amplify the collaborative response of adjacent channels,and a dynamic threshold adjustment mechanism is integrated to adapt to varying interference levels.Validation is carried out on a self-built 64-channel FOD platform by moving a typical metallic foreign object across central,edge,and corner regions,and by conducting comparative tests under different interference intensities.[Result]When a typical metallic foreign object moves to corner regions,the self-inductance disturbance of the detection coil increases from less than 0.02μH to more than 0.06μH,significantly enhancing the discrimination capability at corners.Under varying interference strengths,the dynamic threshold mechanism reduces the number of false positives from 13 to 2,demonstrating good environmental adaptability and stability.[Conclusion]By combining channel differential response,corner enhancement,and dynamic thresholding,the proposed WPT-FOD effectively mitigates edge/corner blind spots and fixed-threshold misjudgment,while providing localization capability and robustness.It markedly improves the accuracy of metallic foreign object detection in WPT systems and offers a feasible path and method reference for the safe application and engineering deployment of WPT systems.
文摘Artificial Intelligence(AI)-native sixth-generation(6G)wireless networks require data-efficient and channel-resilient physical-layer modeling techniques that learn stable device-specific representations under channel variations and hardware imperfections to support secure and reliable device-level authentication under highly dynamic environments.In such networks,massive device heterogeneity and time-varying channel conditions pose significant challenges,as reliable authentication must be achieved with limited labeled data and constrained edge resources.To address this challenge,this paper proposes an Artificial Intelligence(AI)-assisted few-shot physical-layer modeling framework for channel robust device identification,formulated within the paradigm of Specific Emitter Identification(SEI)based on radio frequency(RF)fingerprinting.The proposed framework explicitly formulates few-shot SEI as a channel-resilient physical-layer modeling problem by integrating a lightweight convolutional neural network and Transformer hybrid encoder with a dual-branch feature decoupling mechanism.Device specific RF fingerprints are separated from channel-dependent factors through orthogonality-constrained learning,which effectively suppresses channel-induced prototype drift and stabilizes metric geometry under channel variations.A meta-learned prototypical inference module is further employed under episodic few-shot training,enabling rapid adaptation to new devices and unseen channel conditions using only a small number of labeled samples.Experimental results on multiple realworld RF datasets,including ORACLE Wi-Fi transmitter measurements and civil aviation ADS-B broadcasts(DWi-Fi,DADS-B,and DDF17 ADS-B),demonstrate that the proposed method achieves identification accuracy ranging from 99.1%to 99.8%using only 10 labeled samples per device,while maintaining episode-level performance variance below 0.02.In addition,the proposed model contains approximately 1.45×10^(5) trainable parameters,making it suitable for deployment on resource-constrained edge devices.These results indicate that the proposed framework provides a concrete and scalable AI-driven solution for physical-layer security and device-level authentication in AI-native 6G wireless networks.
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.
基金The Open Access publication fee for this article was fully covered by Abu Dhabi University.
文摘In Wireless Sensor Networks(WSNs),survivability is a crucial issue that is greatly impacted by energy efficiency.Solutions that satisfy application objectives while extending network life are needed to address severe energy constraints inWSNs.This paper presents an Adaptive Enhanced GreyWolf Optimizer(AEGWO)for energy-efficient cluster head(CH)selection that mitigates the exploration–exploitation imbalance,preserves population diversity,and avoids premature convergence inherent in baseline GWO.The AEGWO combines adaptive control of the parameter of the search pressure to accelerate convergence without stagnation,a hybrid velocity-momentum update based on the dynamics of PSO,and an intelligent mutation operator to maintain the diversity of the population.The search is guided by a multi-objective fitness,which aims at maximizing the residual energy,equal distribution of CH,minimizing the intra-cluster distance,desirable proximity to sinks,and enhancing the coverage.Simulations on 100 nodes homogeneousWSN Tested the proposed AEGWO under the same conditions with LEACH,GWO,IGWO,PSO,WOA,and GA,AEGWO significantly increases stability and lifetime compared to LEACHand other tested algorithms;it has the best first,half,and last node dead,and higher residual energy and smaller communication overhead.The findings prove that AEGWO provides sustainable energy management and better lifetime extension,which makes it a robust,flexible clustering protocol of large-scaleWSNs.
基金supported by the National Social Science Foundation of China under Grant 2022-SKJJ-B-112。
文摘To tackle the physical layer security challenges in wireless communication,this paper introduces a multiuser architecture that leverages single-channel blind source separation,centered around a Multi-source Signal Mixture Separator(MSMS).This architecture consists of a multi-user encoder,a channel layer,and a separation decoder,allowing it to handle multiple functions simultaneously,including encoding,modulation,signal separation,demodulation,and decoding.The MSMS receiver effectively enables the separation of numerous user signals,making it exceedingly difficult for unauthorized eavesdroppers to extract valuable information from the mixed signals,thus significantly enhancing communication security.The MSMS can address the challenges of few-shot sample training and achieve joint optimization during transmission by employing a deep learning-based network design.The design of a single receiver reduces system costs and improves spectrum efficiency.The MSMS outperforms traditional Space-time Block Coding(STBC)strategies regarding separation performance,particularly in Block Error Rate(BLER)metrics.Modulation constellation diagrams further analyze the effectiveness of multi-source signal mixture separation.Moreover,this study extends the MSMS framework from a two-user scenario to a three-user scenario,further demonstrating the flexibility and scalability of the proposed architecture.
文摘This paper studies wireless vehicular communication(VehCom)in intelligent transportation systems using an orthogonal frequency division multiplexing with index modulation(OFDM-IM).In the concept of IM,data is transmitted not only through the modulated symbols but also via the indices of the active subcarriers.In contrast to the original OFDM,OFDM-IM activates only non-zero subcarriers,increasing energy efficiency.However,the pilotassisted channel estimation(CE)method is a significant challenge in OFDM-IM,where the desired pilot subcarrier interval is related to the OFDM-IM subblock length.This paper proposes a walsh-scattered pilot-assisted CE for OFDM-IM VehCom.The optimum walsh-scattered pilot assignment is proposed to improve the transmission efficiency.Furthermore,a space-time block code with a high transmit diversity gain is employed for OFDM-IM VehCom to enhance VehCom's signal quality.The results show that the proposed method performs higher CE accuracy and better bit-error rate with significant spectral and energy efficiencies than conventional methods.
文摘The relentless evolution of Information and Communication Technology(ICT) stands as a testament to the synergistic power of collaboration. It thrives at the dynamic intersection of industrial insight, academic rigor, and dedicated research. This special issue, “Achievements of ZTE's Industry-University-Institute Cooperation Projects,” presents a curated collection of cutting-edge research that embodies the fruitful outcomes of deep collaboration, addressing some of the most pressing challenges across wireless communications, artificial intelligence(AI), software engineering, and industrial digitization.
基金funded by Prince Sattam bin Abdulaziz University,grant number PSAU/2025/01/34620.
文摘This paper presents an image encryption scheme for underwater optical wireless communication(UOWC)systems based on dynamically generated hyperchaotic S-boxes,aiming to enhance both data security and transmission performance in underwater environments.The proposed encryption approach provides strong confusion and diffusion properties and is evaluated over five Jerlov water types with different optical attenuation characteristics.Security analysis demonstrates that the encrypted images achieve information entropy values close to the ideal value of 8(7.9925–7.9993),with very low correlation coefficients in horizontal,vertical,and diagonal directions,as well as the system achieves high values in key metrics such as the Unified Average Changing Intensity(UACI)and Number of Pixel Change Rate(NPCR),ranging from 33.42 to 33.47 and from 99.58%to 99.62%,respectively,both near their theoretical optima.In addition to improving confidentiality,the hyperchaotic encryption process decorrelates pixel intensities and redistributes image spectral content,which enhances robustness against underwater absorption and scattering effects.As a result,improved transmission performance is observed;for example,in Jerlov type I(JI)water,the effective transmission distance is extended from16mfor plain images to 24mfor encrypted images,while the Peak Signal to Noise Ratio(PSNR)at 24 m increases from 9.25 to 20.13 dB after decryption and enhancement.These results confirmthat the proposed scheme provides a dual benefit of secure and reliable image transmission in UOWC systems.
基金supported by the National Science and Technology Council,Taiwan,under Grants 113-2221-E-260-014-MY2 and 114-2119-M-033-001.
文摘The rapid growth of mobile and Internet of Things(IoT)applications in dense urban environments places stringent demands on future Beyond 5G(B5G)or Beyond 6G(B6G)networks,which must ensure high Quality of Service(QoS)while maintaining cost-efficiency and sustainable deployment.Traditional strategies struggle with complex 3D propagation,building penetration loss,and the balance between coverage and infrastructure cost.To address this challenge,this study presents the first application of a Global-best Guided Quantum-inspired Tabu Search with Quantum-Not Gate(GQTS-QNG)framework for 3D base-station deployment optimization.The problem is formulated as a multi-objective model that simultaneously maximizes coverage and minimizes deployment cost.A binary-to-decimal encodingmechanism is designed to represent discrete placement coordinates and base station types,leveraging a quantum-inspired method to efficiently search and refine solutions within challenging combinatorial environments.Global-best guidance and tabu memory are integrated to strengthen convergence stability and avoid revisiting previously explored solutions.Simulation results across user densities ranging from 1000 to 10,000 show that GQTS-QNG consistently finds deployment configurations achieving full coverage while reducing deployment cost compared with the state-of-the-art algorithms under equal iteration times.Additionally,our method generates welldistributed and structured Pareto fronts,offering diverse planning options that allow operators to flexibly balance cost and performance requirements.These findings demonstrate that GQTS-QNG is a scalable and efficient algorithm for sustainable 3D cellular network deployment in B5G/6G urban scenarios.
基金National Natural Science Foundation of China(No.62301141)。
文摘The integration of the intelligent reflecting surface(IRS)with simultaneous wireless information and power transfer(SWIPT)has emerged as a cost-effective and efficient solution to enhance the performance of information and energy transfer.In this research,a hybrid active/passive IRS-assisted SWIPT system is proposed.Specifically,an active IRS(AIRS)and a passive IRS(PIRS)are deployed in the SWIPT system to facilitate a multiantenna base station(BS)in simultaneously delivering information and energy to multiple information users(IUs)and energy users(EUs).The objective is to maximize the sum throughput by jointly optimizing the transmitter beamforming and the reflection coefficient matrices of the AIRS and the PIRS while satisfying the transmitter power constraints,the energy harvesting(EH)requirements of EUs,and the AIRS amplification power limitations.However,the optimization variables are highly coupled and cannot be solved directly.To tackle this complex problem,we propose an efficient algorithm based on alternating optimization(AO)and semi-definite relaxation(SDR)techniques to obtain high-quality solutions.Simulation results demonstrate that the hybrid active/passive IRSassisted SWIPT system significantly enhances throughput performance and outperforms benchmark systems.
基金supported in part by the National Natural Science Foundation of China(62522320,92267108,62173322)Liaoning Revitalization Talents Program(XLYC2403062)the Science and Technology Program of Liaoning Province(2023JH3/10200004,2022JH25/10100005)。
文摘The wireless cloud robotic system(WCRS),which fully integrates sensing,communication,computing,and control capabilities as an intelligent agent,is a promising way to achieve intelligent manufacturing due to easy deployment and flexible expansion.However,the high-precision control of WCRS requires deterministic wireless communication,which is always challenging in the complex and dynamic radio space.This paper employs the reconfigurable intelligent surface(RIS)to establish a novel RIS-assisted WCRS architecture,where the radio channel is controlled to achieve ultra-reliable,low-delay,and low-jitter communication for high-precision closed-loop motion control.However,control and communication are strongly coupled and should be co-optimized.Fully considering the constraints of control input threshold,control delay deadline,beam phase,antenna power,and information distortion,we establish a stability maximization problem to jointly optimize control input compensation,RIS phase shift,and beamforming.Herein,a new jitter-oriented system stability objective with respect to control error and communication jitter is defined and the closed-form expression of control delay deadline is derived based on the Jensen Inequality and Lyapunov-Krasovskii functional.Due to the time-varying and partial observability of the channel and robot states,we model the problem as a partially observable Markov decision process(POMDP).To solve this complex problem,we propose a multi-agent transfer reinforcement learning algorithm named LSTM-PPO-MATRL,where the LSTM-enhanced proximal policy optimization(PPO)is designed to approximate an optimal solution and the option-guided policy transfer learning is proposed to facilitate the learning process.By centralized training and decentralized execution,LSTM-PPO-MATRL is validated by extensive experiments on MuJoCo tasks for both low-mobility and high-mobility robotic control scenarios.The results demonstrate that LSTM-PPO-MATRL not only realizes high learning efficiency,but also supports low-delay,low-jitter communication for low error control,where 71.9%control accuracy improvement and 68.7%delay jitter reduction are achieved compared to the PPO-MADRL baseline.
基金funded by the National Natural Science Foundation of China(52475580)the Special Foundation of the Taishan Scholar Project(tsqn202211077,tsqn202311077)+3 种基金Shandong Provincial Excellent Overseas Young Scholar Foundation(2023HWYQ-069)the Shandong Provincial Natural Science Foundation(ZR2023ME118,ZR2023QF080)the Natural Science Foundation of Qingdao City(23-2-1-219-zyyd-jch,23-2-1-111-zyyd-jch)the Fundamental Research Funds for the Central Universities(23CX06032A).
文摘The complex wiring,bulky data collection devices,and difficulty in fast and on-site data interpretation significantly limit the practical application of flexible strain sensors as wearable devices.To tackle these challenges,this work develops an artificial intelligenceassisted,wireless,flexible,and wearable mechanoluminescent strain sensor system(AIFWMLS)by integration of deep learning neural network-based color data processing system(CDPS)with a sandwich-structured flexible mechanoluminescent sensor(SFLC)film.The SFLC film shows remarkable and robust mechanoluminescent performance with a simple structure for easy fabrication.The CDPS system can rapidly and accurately extract and interpret the color of the SFLC film to strain values with auto-correction of errors caused by the varying color temperature,which significantly improves the accuracy of the predicted strain.A smart glove mechanoluminescent sensor system demonstrates the great potential of the AIFWMLS system in human gesture recognition.Moreover,the versatile SFLC film can also serve as a encryption device.The integration of deep learning neural network-based artificial intelligence and SFLC film provides a promising strategy to break the“color to strain value”bottleneck that hinders the practical application of flexible colorimetric strain sensors,which could promote the development of wearable and flexible strain sensors from laboratory research to consumer markets.
