A new approach for peak-to-average power ratio (PAPR) reduction in orthogonal frequency division multiplexing (OFDM) systems was proposed.This approach is based on assigning powers to the different subcarriers of OFDM...A new approach for peak-to-average power ratio (PAPR) reduction in orthogonal frequency division multiplexing (OFDM) systems was proposed.This approach is based on assigning powers to the different subcarriers of OFDM using an unequal power distribution strategy.In addition,a reduced complexity selective mapping (RC-SLM) scheme was proposed.The proposed scheme is based on partitioning the frequency domain symbol sequence into several sub-blocks,and then each sub-block is multiplied by different phase sequences whose length is shorter than that used in the conventional SLM scheme.Then,a kind of low complexity conversions is used to replace the IFFT blocks.The performance of the proposed RC-SLM scheme along with the new approach was studied with computer simulation.The obtained results show that the proposed RC-SLM scheme is able to achieve the lowest computational complexity when compared with other low complexity schemes proposed in the literature while at the same time improves the PAPR reduction performance by about 0.3 dB.展开更多
Landslide susceptibility mapping is a crucial tool for disaster prevention and management.The performance of conventional data-driven model is greatly influenced by the quality of the samples data.The random selection...Landslide susceptibility mapping is a crucial tool for disaster prevention and management.The performance of conventional data-driven model is greatly influenced by the quality of the samples data.The random selection of negative samples results in the lack of interpretability throughout the assessment process.To address this limitation and construct a high-quality negative samples database,this study introduces a physics-informed machine learning approach,combining the random forest model with Scoops 3D,to optimize the negative samples selection strategy and assess the landslide susceptibility of the study area.The Scoops 3D is employed to determine the factor of safety value leveraging Bishop’s simplified method.Instead of conventional random selection,negative samples are extracted from the areas with a high factor of safety value.Subsequently,the results of conventional random forest model and physics-informed data-driven model are analyzed and discussed,focusing on model performance and prediction uncertainty.In comparison to conventional methods,the physics-informed model,set with a safety area threshold of 3,demonstrates a noteworthy improvement in the mean AUC value by 36.7%,coupled with a reduced prediction uncertainty.It is evident that the determination of the safety area threshold exerts an impact on both prediction uncertainty and model performance.展开更多
文摘A new approach for peak-to-average power ratio (PAPR) reduction in orthogonal frequency division multiplexing (OFDM) systems was proposed.This approach is based on assigning powers to the different subcarriers of OFDM using an unequal power distribution strategy.In addition,a reduced complexity selective mapping (RC-SLM) scheme was proposed.The proposed scheme is based on partitioning the frequency domain symbol sequence into several sub-blocks,and then each sub-block is multiplied by different phase sequences whose length is shorter than that used in the conventional SLM scheme.Then,a kind of low complexity conversions is used to replace the IFFT blocks.The performance of the proposed RC-SLM scheme along with the new approach was studied with computer simulation.The obtained results show that the proposed RC-SLM scheme is able to achieve the lowest computational complexity when compared with other low complexity schemes proposed in the literature while at the same time improves the PAPR reduction performance by about 0.3 dB.
基金Project(G2022165004L)supported by the High-end Foreign Expert Introduction Program,ChinaProject(2021XM3008)supported by the Special Foundation of Postdoctoral Support Program,Chongqing,China+1 种基金Project(2018-ZL-01)supported by the Sichuan Transportation Science and Technology Project,ChinaProject(HZ2021001)supported by the Chongqing Municipal Education Commission,China。
文摘Landslide susceptibility mapping is a crucial tool for disaster prevention and management.The performance of conventional data-driven model is greatly influenced by the quality of the samples data.The random selection of negative samples results in the lack of interpretability throughout the assessment process.To address this limitation and construct a high-quality negative samples database,this study introduces a physics-informed machine learning approach,combining the random forest model with Scoops 3D,to optimize the negative samples selection strategy and assess the landslide susceptibility of the study area.The Scoops 3D is employed to determine the factor of safety value leveraging Bishop’s simplified method.Instead of conventional random selection,negative samples are extracted from the areas with a high factor of safety value.Subsequently,the results of conventional random forest model and physics-informed data-driven model are analyzed and discussed,focusing on model performance and prediction uncertainty.In comparison to conventional methods,the physics-informed model,set with a safety area threshold of 3,demonstrates a noteworthy improvement in the mean AUC value by 36.7%,coupled with a reduced prediction uncertainty.It is evident that the determination of the safety area threshold exerts an impact on both prediction uncertainty and model performance.
