The western Los Angeles(LA)wildfires of early January 2025 caused catastrophic social and environmental impacts,drawing widespread attention.This study investigates the characteristics of these wildfires and quantifie...The western Los Angeles(LA)wildfires of early January 2025 caused catastrophic social and environmental impacts,drawing widespread attention.This study investigates the characteristics of these wildfires and quantifies the influence of heat and drought on their likelihood using a copula-based Bayesian probability framework.The wildfires were characterized by burned area(BA)and intensity(fire radiative power,FRP).The criteria establishing the presence of“hot drought”conditions were identified using the 5-day Standardized Temperature Index(STI)and 75-day Standardized Precipitation Index(SPI),respectively.The wildfire outbreak began on 7 January 2025 and burned for more than six days,with the total burned area exceeding 245 km^(2) and the cumulative FRP exceeding 41060 MW.Based on satellite-derived active fire observations from 2001 to 2025,we estimate that such large and intense wildfires during LA’s rainy season represent a once-in-a-67-year event.The wildfires were largely driven by the combination of hot and dry conditions,which dried out soils and vegetation that had proliferated due to above-average precipitation in previous winter seasons,thereby providing abundant fuel.Our seasonal analysis reveals that extreme drought increased the probability of wildfires matching the 2025 intensity and BA by 54%and 75%,respectively.Hot drought further amplified these probabilities by 149%(intensity)and 210%(BA).These findings suggest an elevated risk of large wildfires under hot drought conditions,contributing to their expansion into the non-traditional fire season.展开更多
Cell-free massive multiple-input multipleoutput(MIMO)is a promising technology for future wireless communications,where a large number of distributed access points(APs)simultaneously serve all users over the same time...Cell-free massive multiple-input multipleoutput(MIMO)is a promising technology for future wireless communications,where a large number of distributed access points(APs)simultaneously serve all users over the same time-frequency resources.Since users and APs may locate close to each other,the line-of-sight(Lo S)transmission occurs more frequently in cell-free massive MIMO systems.Hence,in this paper,we investigate the cell-free massive MIMO system with Lo S and non-line-of-sight(NLo S)transmissions,where APs and users are both distributed according to Poisson point process.Using tools from stochastic geometry,we derive a tight lower bound for the user downlink achievable rate and we further obtain the energy efficiency(EE)by considering the power consumption on downlink payload transmissions and circuitry dissipation.Based on the analysis,the optimal AP density and AP antenna number that maximize the EE are obtained.It is found that compared with the previous work that only considers NLo S transmissions,the actual optimal AP density should be much smaller,and the maximized EE is actually much higher.展开更多
Visibility conditions between antennas,i.e.Line-of-Sight(LOS)and Non-Line-of-Sight(NLOS)can be crucial in the context of indoor localization,for which detecting the NLOS condition and further correcting constant posit...Visibility conditions between antennas,i.e.Line-of-Sight(LOS)and Non-Line-of-Sight(NLOS)can be crucial in the context of indoor localization,for which detecting the NLOS condition and further correcting constant position estimation errors or allocating resources can reduce the negative influence of multipath propagation on wireless communication and positioning.In this paper a Deep Learning(DL)model to classify LOS/NLOS condition while analyzing two Channel Impulse Response(CIR)parameters:Total Power(TP)[dBm]and First Path Power(FP)[dBm]is proposed.The experiments were conducted using DWM1000 DecaWave radio module based on measurements collected in a real indoor environment and the proposed architecture provides LOS/NLOS identification with an accuracy of more than 100%and 95%in static and dynamic senarios,respectively.The proposed model improves the classification rate by 2-5%compared to other Machine Learning(ML)methods proposed in the literature.展开更多
本代宽带无线接入系统(BWA)基于视线距离传输(Line of Sight,即LOS)的工作模式,具有覆盖率不高等不足,该文介绍了基于非视线距离传输技术(None Line of Sight)的下一代BWA的优点和技术难点,重点介绍了NLOS传输所采用的OFDM、多天线等关...本代宽带无线接入系统(BWA)基于视线距离传输(Line of Sight,即LOS)的工作模式,具有覆盖率不高等不足,该文介绍了基于非视线距离传输技术(None Line of Sight)的下一代BWA的优点和技术难点,重点介绍了NLOS传输所采用的OFDM、多天线等关键技术,最后,对BWA的两种技术演进路线作了比较。展开更多
The dominant error source of mobile terminal location in wireless sensor networks （WSNs） is the non-line-of-sight （NLOS） propagation error. Among the algorithms proposed to mitigate the influence of NLOS propagati...The dominant error source of mobile terminal location in wireless sensor networks （WSNs） is the non-line-of-sight （NLOS） propagation error. Among the algorithms proposed to mitigate the influence of NLOS propagation error, residual test （RT） is an efficient one, however with high computational complexity （CC）. An improved algorithm that memorizes the light of sight （LOS） range measurements （RMs） identified memorize LOS range measurements identified residual test （MLSI-RT） is presented in this paper to address this problem. The MLSI-RT is based on the assumption that when all RMs are from LOS propagations, the normalized residual follows the central Chi-Square distribution while for NLOS cases it is non-central. This study can reduce the CC by more than 90%.展开更多
针对视距(Line of Sight,LOS)和非视距(None-Line of Sight,NLOS)混合环境机动目标跟踪问题,提出一种基于"当前"统计模型(current statistical,CS)和无迹卡尔曼滤波(unscented Kalman filter,UKF)的交互式多模型方法(IMM-UKF-...针对视距(Line of Sight,LOS)和非视距(None-Line of Sight,NLOS)混合环境机动目标跟踪问题,提出一种基于"当前"统计模型(current statistical,CS)和无迹卡尔曼滤波(unscented Kalman filter,UKF)的交互式多模型方法(IMM-UKF-CS)。该方法在交互式多模型的框架内,利用CS在机动目标跟踪方面的优势,并选择具有较高跟踪精度且计算代价较低的UKF作为子滤波器。仿真结果表明:在LOS/NLOS混合环境中,IMM-UKF-CS具有较高的跟踪精度、较强的鲁棒性及较低的时间代价,具有良好的应用价值。展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.42471034,42330604)the Qing Lan Projectsupport from the National Key Scientific and Technological Infrastructure project“Earth System Numerical Simulation Facility”(EarthLab).
文摘The western Los Angeles(LA)wildfires of early January 2025 caused catastrophic social and environmental impacts,drawing widespread attention.This study investigates the characteristics of these wildfires and quantifies the influence of heat and drought on their likelihood using a copula-based Bayesian probability framework.The wildfires were characterized by burned area(BA)and intensity(fire radiative power,FRP).The criteria establishing the presence of“hot drought”conditions were identified using the 5-day Standardized Temperature Index(STI)and 75-day Standardized Precipitation Index(SPI),respectively.The wildfire outbreak began on 7 January 2025 and burned for more than six days,with the total burned area exceeding 245 km^(2) and the cumulative FRP exceeding 41060 MW.Based on satellite-derived active fire observations from 2001 to 2025,we estimate that such large and intense wildfires during LA’s rainy season represent a once-in-a-67-year event.The wildfires were largely driven by the combination of hot and dry conditions,which dried out soils and vegetation that had proliferated due to above-average precipitation in previous winter seasons,thereby providing abundant fuel.Our seasonal analysis reveals that extreme drought increased the probability of wildfires matching the 2025 intensity and BA by 54%and 75%,respectively.Hot drought further amplified these probabilities by 149%(intensity)and 210%(BA).These findings suggest an elevated risk of large wildfires under hot drought conditions,contributing to their expansion into the non-traditional fire season.
基金supported in part by the National Natural Science Foundation of China under Grant 62171231in part by the Jiangsu Provincial Key Research and Development Program(No.BE2020084-1)。
文摘Cell-free massive multiple-input multipleoutput(MIMO)is a promising technology for future wireless communications,where a large number of distributed access points(APs)simultaneously serve all users over the same time-frequency resources.Since users and APs may locate close to each other,the line-of-sight(Lo S)transmission occurs more frequently in cell-free massive MIMO systems.Hence,in this paper,we investigate the cell-free massive MIMO system with Lo S and non-line-of-sight(NLo S)transmissions,where APs and users are both distributed according to Poisson point process.Using tools from stochastic geometry,we derive a tight lower bound for the user downlink achievable rate and we further obtain the energy efficiency(EE)by considering the power consumption on downlink payload transmissions and circuitry dissipation.Based on the analysis,the optimal AP density and AP antenna number that maximize the EE are obtained.It is found that compared with the previous work that only considers NLo S transmissions,the actual optimal AP density should be much smaller,and the maximized EE is actually much higher.
基金supported under ministry subsidy for research for Gdansk University of Technology。
文摘Visibility conditions between antennas,i.e.Line-of-Sight(LOS)and Non-Line-of-Sight(NLOS)can be crucial in the context of indoor localization,for which detecting the NLOS condition and further correcting constant position estimation errors or allocating resources can reduce the negative influence of multipath propagation on wireless communication and positioning.In this paper a Deep Learning(DL)model to classify LOS/NLOS condition while analyzing two Channel Impulse Response(CIR)parameters:Total Power(TP)[dBm]and First Path Power(FP)[dBm]is proposed.The experiments were conducted using DWM1000 DecaWave radio module based on measurements collected in a real indoor environment and the proposed architecture provides LOS/NLOS identification with an accuracy of more than 100%and 95%in static and dynamic senarios,respectively.The proposed model improves the classification rate by 2-5%compared to other Machine Learning(ML)methods proposed in the literature.
文摘本代宽带无线接入系统(BWA)基于视线距离传输(Line of Sight,即LOS)的工作模式,具有覆盖率不高等不足,该文介绍了基于非视线距离传输技术(None Line of Sight)的下一代BWA的优点和技术难点,重点介绍了NLOS传输所采用的OFDM、多天线等关键技术,最后,对BWA的两种技术演进路线作了比较。
基金supported by the State Key Program of National Natural Science of China (Grant No.60532030)the New Century Excellent Talents in University (Grant No.NCET-08-0333)the Natural Science Foundation of Shandong Province (Grant No.Y2007G10)
文摘The dominant error source of mobile terminal location in wireless sensor networks （WSNs） is the non-line-of-sight （NLOS） propagation error. Among the algorithms proposed to mitigate the influence of NLOS propagation error, residual test （RT） is an efficient one, however with high computational complexity （CC）. An improved algorithm that memorizes the light of sight （LOS） range measurements （RMs） identified memorize LOS range measurements identified residual test （MLSI-RT） is presented in this paper to address this problem. The MLSI-RT is based on the assumption that when all RMs are from LOS propagations, the normalized residual follows the central Chi-Square distribution while for NLOS cases it is non-central. This study can reduce the CC by more than 90%.
文摘针对视距(Line of Sight,LOS)和非视距(None-Line of Sight,NLOS)混合环境机动目标跟踪问题,提出一种基于"当前"统计模型(current statistical,CS)和无迹卡尔曼滤波(unscented Kalman filter,UKF)的交互式多模型方法(IMM-UKF-CS)。该方法在交互式多模型的框架内,利用CS在机动目标跟踪方面的优势,并选择具有较高跟踪精度且计算代价较低的UKF作为子滤波器。仿真结果表明:在LOS/NLOS混合环境中,IMM-UKF-CS具有较高的跟踪精度、较强的鲁棒性及较低的时间代价,具有良好的应用价值。
