The multi-mode integrated railway system,anchored by the high-speed railway,caters to the diverse travel requirements both within and between cities,offering safe,comfortable,punctual,and eco-friendly transportation s...The multi-mode integrated railway system,anchored by the high-speed railway,caters to the diverse travel requirements both within and between cities,offering safe,comfortable,punctual,and eco-friendly transportation services.With the expansion of the railway networks,enhancing the efficiency and safety of the comprehensive system has become a crucial issue in the advanced development of railway transportation.In light of the prevailing application of artificial intelligence technologies within railway systems,this study leverages large model technology characterized by robust learning capabilities,efficient associative abilities,and linkage analysis to propose an Artificial-intelligent(AI)-powered railway control and dispatching system.This system is elaborately designed with four core functions,including global optimum unattended dispatching,synergetic transportation in multiple modes,high-speed automatic control,and precise maintenance decision and execution.The deployment pathway and essential tasks of the system are further delineated,alongside the challenges and obstacles encountered.The AI-powered system promises a significant enhancement in the operational efficiency and safety of the composite railway system,ensuring a more effective alignment between transportation services and passenger demands.展开更多
The deployment of multiple intelligent reflecting surfaces(IRSs)in blockage-prone millimeter wave(mmWave)communication networks have garnered considerable attention lately.Despite the remarkably low circuit power cons...The deployment of multiple intelligent reflecting surfaces(IRSs)in blockage-prone millimeter wave(mmWave)communication networks have garnered considerable attention lately.Despite the remarkably low circuit power consumption per IRS element,the aggregate energy consumption becomes substantial if all elements of an IRS are turned on given a considerable number of IRSs,resulting in lower overall energy efficiency(EE).To tackle this challenge,we propose a flexible and efficient approach that individually controls the status of each IRS element.Specifically,the network EE is maximized by jointly optimizing the associations of base stations(BSs)and user equipments(UEs),transmit beamforming,phase shifts of IRS elements,and the associations of individual IRS elements and UEs.The problem is efficiently addressed in two phases.First,the Gale-Shapley algorithm is applied for BS-UE association,followed by a block coordinate descent-based algorithm that iteratively solves the subproblems related to active beamforming,phase shifts,and element-UE associations.To reduce the tremendous dimensionality of optimization variables introduced by element-UE associations in large-scale IRS networks,we introduce an efficient algorithm to solve the associations between IRS elements and UEs.Numerical results show that the proposed elementwise control scheme improves EE by 34.24% compared to the network with IRS-all-on scheme.展开更多
Intelligent reflecting surface(IRS)is a newly emerged and promising paradigm to substantially improve the performance of wireless communications by constructing favorable communication channels via properly tuning mas...Intelligent reflecting surface(IRS)is a newly emerged and promising paradigm to substantially improve the performance of wireless communications by constructing favorable communication channels via properly tuning massive reflecting elements.This paper considers a distributed IRS aided decode-and-forward(DF)relaying system over Nakagami-m fading channels.Based on a tight approximation for the distribution of the received signalto-noise ratio(SNR),we first derive exact closed-form expressions of the outage probability,ergodic capacity,and energy efficiency for the considered system.Moreover,we propose the optimal IRS configuration considering the energy efficiency and pilot overhead.Finally,we compare the performance between the distributed IRS-aided DF relaying and multi-IRS-only systems,and verify the analytical results by using monte carlo simulations.展开更多
In this paper,we develop a 6G wireless powered Internet of Things(IoT)system assisted by unmanned aerial vehicles(UAVs)to intelligently supply energy and collect data at the same time.In our dual-UAV scheme,UAV-E,with...In this paper,we develop a 6G wireless powered Internet of Things(IoT)system assisted by unmanned aerial vehicles(UAVs)to intelligently supply energy and collect data at the same time.In our dual-UAV scheme,UAV-E,with a constant power supply,transmits energy to charge the IoT devices on the ground,whereas UAV-B serves the IoT devices by data collection as a base station.In this framework,the system's energy efficiency is maximized,which we define as a ratio of the sum rate of IoT devices to the energy consumption of two UAVs during a fixed working duration.With the constraints of duration,transmit power,energy,and mobility,a difficult non-convex issue is presented by optimizing the trajectory,time duration allocation,and uplink transmit power of concurrently.To tackle the non-convex fractional optimization issue,we deconstruct it into three subproblems and we solve each of them iteratively using the descent method in conjunction with sequential convex approximation(SCA)approaches and the Dinkelbach algorithm.The simulation findings indicate that the suggested cooperative design has the potential to greatly increase the energy efficiency of the 6G intelligent UAV-assisted wireless powered IoT system when compared to previous benchmark systems.展开更多
基金supported by the National Key R&D Program of China(2022YFB4300500).
文摘The multi-mode integrated railway system,anchored by the high-speed railway,caters to the diverse travel requirements both within and between cities,offering safe,comfortable,punctual,and eco-friendly transportation services.With the expansion of the railway networks,enhancing the efficiency and safety of the comprehensive system has become a crucial issue in the advanced development of railway transportation.In light of the prevailing application of artificial intelligence technologies within railway systems,this study leverages large model technology characterized by robust learning capabilities,efficient associative abilities,and linkage analysis to propose an Artificial-intelligent(AI)-powered railway control and dispatching system.This system is elaborately designed with four core functions,including global optimum unattended dispatching,synergetic transportation in multiple modes,high-speed automatic control,and precise maintenance decision and execution.The deployment pathway and essential tasks of the system are further delineated,alongside the challenges and obstacles encountered.The AI-powered system promises a significant enhancement in the operational efficiency and safety of the composite railway system,ensuring a more effective alignment between transportation services and passenger demands.
基金supported by the National Natural Science Foundation of China under grant U22A2003 and 62271515Shenzhen Science and Technology Program under grant ZDSYS20210623091807023supported by the National Natural Science Foundation of China under Grant 62301300.
文摘The deployment of multiple intelligent reflecting surfaces(IRSs)in blockage-prone millimeter wave(mmWave)communication networks have garnered considerable attention lately.Despite the remarkably low circuit power consumption per IRS element,the aggregate energy consumption becomes substantial if all elements of an IRS are turned on given a considerable number of IRSs,resulting in lower overall energy efficiency(EE).To tackle this challenge,we propose a flexible and efficient approach that individually controls the status of each IRS element.Specifically,the network EE is maximized by jointly optimizing the associations of base stations(BSs)and user equipments(UEs),transmit beamforming,phase shifts of IRS elements,and the associations of individual IRS elements and UEs.The problem is efficiently addressed in two phases.First,the Gale-Shapley algorithm is applied for BS-UE association,followed by a block coordinate descent-based algorithm that iteratively solves the subproblems related to active beamforming,phase shifts,and element-UE associations.To reduce the tremendous dimensionality of optimization variables introduced by element-UE associations in large-scale IRS networks,we introduce an efficient algorithm to solve the associations between IRS elements and UEs.Numerical results show that the proposed elementwise control scheme improves EE by 34.24% compared to the network with IRS-all-on scheme.
基金supported in part by National Natural Science Foundation of China under Grant 62371262 and 61971467in part by the Key Research and Development Program of Jiangsu Province of China under Grant BE2021013-1+1 种基金in part by the Qinlan Project of Jiangsu Provincein part by the Scientific Research Program of Nantong under Grant JC22022026
文摘Intelligent reflecting surface(IRS)is a newly emerged and promising paradigm to substantially improve the performance of wireless communications by constructing favorable communication channels via properly tuning massive reflecting elements.This paper considers a distributed IRS aided decode-and-forward(DF)relaying system over Nakagami-m fading channels.Based on a tight approximation for the distribution of the received signalto-noise ratio(SNR),we first derive exact closed-form expressions of the outage probability,ergodic capacity,and energy efficiency for the considered system.Moreover,we propose the optimal IRS configuration considering the energy efficiency and pilot overhead.Finally,we compare the performance between the distributed IRS-aided DF relaying and multi-IRS-only systems,and verify the analytical results by using monte carlo simulations.
基金supported by the Natural Science Foundation of Beijing Municipality under Grant L192034。
文摘In this paper,we develop a 6G wireless powered Internet of Things(IoT)system assisted by unmanned aerial vehicles(UAVs)to intelligently supply energy and collect data at the same time.In our dual-UAV scheme,UAV-E,with a constant power supply,transmits energy to charge the IoT devices on the ground,whereas UAV-B serves the IoT devices by data collection as a base station.In this framework,the system's energy efficiency is maximized,which we define as a ratio of the sum rate of IoT devices to the energy consumption of two UAVs during a fixed working duration.With the constraints of duration,transmit power,energy,and mobility,a difficult non-convex issue is presented by optimizing the trajectory,time duration allocation,and uplink transmit power of concurrently.To tackle the non-convex fractional optimization issue,we deconstruct it into three subproblems and we solve each of them iteratively using the descent method in conjunction with sequential convex approximation(SCA)approaches and the Dinkelbach algorithm.The simulation findings indicate that the suggested cooperative design has the potential to greatly increase the energy efficiency of the 6G intelligent UAV-assisted wireless powered IoT system when compared to previous benchmark systems.
