NonorthogonalMultiple Access(NOMA)is incorporated into the wireless network systems to achieve better connectivity,spectral and energy effectiveness,higher data transfer rate,and also obtain the high quality of servic...NonorthogonalMultiple Access(NOMA)is incorporated into the wireless network systems to achieve better connectivity,spectral and energy effectiveness,higher data transfer rate,and also obtain the high quality of services(QoS).In order to improve throughput and minimum latency,aMultivariate Renkonen Regressive Weighted Preference Bootstrap Aggregation based Nonorthogonal Multiple Access(MRRWPBA-NOMA)technique is introduced for network communication.In the downlink transmission,each mobile device’s resources and their characteristics like energy,bandwidth,and trust are measured.Followed by,the Weighted Preference Bootstrap Aggregation is applied to recognize the resource-efficient mobile devices for aware data transmission by constructing the different weak hypotheses i.e.,Multivariate Renkonen Regression functions.Based on the classification,resource and trust-aware devices are selected for transmission.Simulation of the proposed MRRWPBA-NOMA technique and existing methods are carried out with different metrics such as data delivery ratio,throughput,latency,packet loss rate,and energy efficiency,signaling overhead.The simulation results assessment indicates that the proposed MRRWPBA-NOMA outperforms well than the conventional methods.展开更多
Information on the population distribution at the building scale can help governments make supplemental decisions to address complex urban management issues.However,the discontinuity and strong spatial heterogeneity o...Information on the population distribution at the building scale can help governments make supplemental decisions to address complex urban management issues.However,the discontinuity and strong spatial heterogeneity of research units at the building scale make it challenging to fuse multi-source geographic data,which causes significant errors in population estimation.To address this problem,this study proposes a method for population estimation at the building scale based on Dual-Environment Feature Fusion(DEFF).The dual environments of buildings were constructed by splitting the physical boundaries and extracting features suitable for the dual-environment scale from multi-source geographic data to describe the complex environmental features of buildings.Meanwhile,Data Quality Weighting based Technique for Order of Preference by Similarity to Ideal Solution(DQW-TOPSIS)method was proposed to assign appropriate weights to the features of the external environment for better feature fusion.Finally,a regression model was established using dual-environment features for building-scale population estimation.The experimental areas chosen for this study were Jianghan and Wuchang Districts,both located in Wuhan City,China.The estimated results of the DEFF were compared with those of the ablation experiments,as well as three publicly accessible population datasets,specifically LandScan,WorldPop,and GHS-POP,at the community scale.The evaluation results showed that DEFF had an R2 of approximately 0.8,Mean Absolute Error(MAE)of approximately 1200,Root Mean Square Error(RMSE)of approximately 1700,and both Mean Absolute Percentage Error(MAPE)and Symmetric Mean Absolute Percentage Error(SMAPE)of approximately 26%,indicating an improved performance and verifying the validity of the proposed method for fine-scale population estimation.展开更多
基金the Taif University Researchers Supporting Project number(TURSP-2020/36),Taif University,Taif,Saudi Arabiafundedby Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R97), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia。
文摘NonorthogonalMultiple Access(NOMA)is incorporated into the wireless network systems to achieve better connectivity,spectral and energy effectiveness,higher data transfer rate,and also obtain the high quality of services(QoS).In order to improve throughput and minimum latency,aMultivariate Renkonen Regressive Weighted Preference Bootstrap Aggregation based Nonorthogonal Multiple Access(MRRWPBA-NOMA)technique is introduced for network communication.In the downlink transmission,each mobile device’s resources and their characteristics like energy,bandwidth,and trust are measured.Followed by,the Weighted Preference Bootstrap Aggregation is applied to recognize the resource-efficient mobile devices for aware data transmission by constructing the different weak hypotheses i.e.,Multivariate Renkonen Regression functions.Based on the classification,resource and trust-aware devices are selected for transmission.Simulation of the proposed MRRWPBA-NOMA technique and existing methods are carried out with different metrics such as data delivery ratio,throughput,latency,packet loss rate,and energy efficiency,signaling overhead.The simulation results assessment indicates that the proposed MRRWPBA-NOMA outperforms well than the conventional methods.
基金supported by the National Natural Science Foundation of China[Grant numbers U20A2091,41930107]。
文摘Information on the population distribution at the building scale can help governments make supplemental decisions to address complex urban management issues.However,the discontinuity and strong spatial heterogeneity of research units at the building scale make it challenging to fuse multi-source geographic data,which causes significant errors in population estimation.To address this problem,this study proposes a method for population estimation at the building scale based on Dual-Environment Feature Fusion(DEFF).The dual environments of buildings were constructed by splitting the physical boundaries and extracting features suitable for the dual-environment scale from multi-source geographic data to describe the complex environmental features of buildings.Meanwhile,Data Quality Weighting based Technique for Order of Preference by Similarity to Ideal Solution(DQW-TOPSIS)method was proposed to assign appropriate weights to the features of the external environment for better feature fusion.Finally,a regression model was established using dual-environment features for building-scale population estimation.The experimental areas chosen for this study were Jianghan and Wuchang Districts,both located in Wuhan City,China.The estimated results of the DEFF were compared with those of the ablation experiments,as well as three publicly accessible population datasets,specifically LandScan,WorldPop,and GHS-POP,at the community scale.The evaluation results showed that DEFF had an R2 of approximately 0.8,Mean Absolute Error(MAE)of approximately 1200,Root Mean Square Error(RMSE)of approximately 1700,and both Mean Absolute Percentage Error(MAPE)and Symmetric Mean Absolute Percentage Error(SMAPE)of approximately 26%,indicating an improved performance and verifying the validity of the proposed method for fine-scale population estimation.
