The Poisson point process(PPP) has been widely used in wireless network modeling and performance analysis due to the independence between its nodes. Therefore, it may not be a suitable model for many of the exclusive ...The Poisson point process(PPP) has been widely used in wireless network modeling and performance analysis due to the independence between its nodes. Therefore, it may not be a suitable model for many of the exclusive networks between the nodes. This paper analyzes the energy efficiency(EE) and optimizes the two-tier heterogeneous cellular networks(Het Nets). Considering the mutual exclusion between macro base stations(MBSs) distribution, the deployment of MBSs is modeled by the Matérn hard-core point process(MHCPP), and the deployment of pico base stations(PBSs) is modeled by the PPP. We adopt a simple approximation method to study the signal to interference ratio(SIR) distribution in two-tier MHCPP-PPP networks and then derive the coverage probabilities, the average data rates and the energy efficiency of Het Nets. Finally, an optimization algorithm is proposed to improve the EE of Het Nets by controlling the transmit power of PBSs. The simulation results show that the EE of a system can be effectively improved by selecting the appropriate transmit power for the PBSs. In addition, two-tier MHCPP-PPP Het Nets have higher energy efficiency than two-tier PPP-PPP Het Nets.展开更多
Background:The local pivotal method(LPM)utilizing auxiliary data in sample selection has recently been proposed as a sampling method for national forest inventories(NFIs).Its performance compared to simple random samp...Background:The local pivotal method(LPM)utilizing auxiliary data in sample selection has recently been proposed as a sampling method for national forest inventories(NFIs).Its performance compared to simple random sampling(SRS)and LPM with geographical coordinates has produced promising results in simulation studies.In this simulation study we compared all these sampling methods to systematic sampling.The LPM samples were selected solely using the coordinates(LPMxy)or,in addition to that,auxiliary remote sensing-based forest variables(RS variables).We utilized field measurement data(NFI-field)and Multi-Source NFI(MS-NFI)maps as target data,and independent MS-NFI maps as auxiliary data.The designs were compared using relative efficiency(RE);a ratio of mean squared errors of the reference sampling design against the studied design.Applying a method in NFI also requires a proven estimator for the variance.Therefore,three different variance estimators were evaluated against the empirical variance of replications:1)an estimator corresponding to SRS;2)a Grafström-Schelin estimator repurposed for LPM;and 3)a Matérn estimator applied in the Finnish NFI for systematic sampling design.Results:The LPMxy was nearly comparable with the systematic design for the most target variables.The REs of the LPM designs utilizing auxiliary data compared to the systematic design varied between 0.74–1.18,according to the studied target variable.The SRS estimator for variance was expectedly the most biased and conservative estimator.Similarly,the Grafström-Schelin estimator gave overestimates in the case of LPMxy.When the RS variables were utilized as auxiliary data,the Grafström-Schelin estimates tended to underestimate the empirical variance.In systematic sampling the Matérn and Grafström-Schelin estimators performed for practical purposes equally.Conclusions:LPM optimized for a specific variable tended to be more efficient than systematic sampling,but all of the considered LPM designs were less efficient than the systematic sampling design for some target variables.The Grafström-Schelin estimator could be used as such with LPMxy or instead of the Matérn estimator in systematic sampling.Further studies of the variance estimators are needed if other auxiliary variables are to be used in LPM.展开更多
Interference management is one of the most important issues in the device-to-device(D2D)-enabled heterogeneous cellular networks(HetCNets)due to the coexistence of massive cellular and D2D devices in which D2D devices...Interference management is one of the most important issues in the device-to-device(D2D)-enabled heterogeneous cellular networks(HetCNets)due to the coexistence of massive cellular and D2D devices in which D2D devices reuse the cellular spectrum.To alleviate the interference,an efficient interference management way is to set exclusion zones around the cellular receivers.In this paper,we adopt a stochastic geometry approach to analyze the outage probabilities of cellular and D2D users in the D2D-enabled HetCNets.The main difficulties contain three aspects:1)how to model the location randomness of base stations,cellular and D2D users in practical networks;2)how to capture the randomness and interrelation of cellular and D2D transmissions due to the existence of random exclusion zones;3)how to characterize the different types of interference and their impacts on the outage probabilities of cellular and D2D users.We then run extensive Monte-Carlo simulations which manifest that our theoretical model is very accurate.展开更多
Background Accurate measurements of aboveground biomass(AGB)are essential for understanding the planet's carbon balance.The Atlantic Forest of the Serra do Mar in southeastern Brazil contains large areas of well-p...Background Accurate measurements of aboveground biomass(AGB)are essential for understanding the planet's carbon balance.The Atlantic Forest of the Serra do Mar in southeastern Brazil contains large areas of well-preserved remnants,characterized by mountainous terrain with significant orographic contrasts along its elevation gradient.This diverse landscape creates a variety of biophysical factors that strongly influence the spatial distribution of AGB.This study aims to estimate AGB using a hybrid geostatistical methodology,regression kriging simulation(RKS),to analyze AGB spatial distribution at a local scale(84 plots,each 0.01 ha)across a small forest fragment covering the entire tree-covered area(8777 ha).Building on traditional regression kriging method,this study introduces an innovative approach by incorporating Gaussian simulation to interpolate residuals,allowing RKS to account for uncertainties in the estimation process and create new results.This allows us to clearly distinguish exogenous ecological processes from endogenous ones before reaching the model's final estimate.Results Four regression kriging models were created,and the best-performing model used the Enhanced Vegetation Index and direct solar radiation(DSR),achieving an R^(2) of 55%.A Gaussian simulation was performed to interpolate the residuals of this model.The final results indicate that RKS provides accurate AGB estimates(RMSE=1.333 Mg/0.01 ha and R^(2) of 77%).Additionally,the inclusion of DSR as a new predictor variable enhances the precision of AGB estimates.The analysis showed that 63%of the sample pairs exhibited measurable spatial dependence.Conclusions Regression kriging simulation is proposed using Gaussian simulation,altering the classical application of regression kriging.For this,a case study was conducted in the Atlantic Forest of Serra do Mar to estimate the spatial distribution of tree biomass in a forest fragment of this region.We demonstrate that the proposed method better captures the heterogeneity of the region and produces more comprehensive results than regression kriging.Regression kriging simulation estimates tree biomass by considering the actual fluctuations of the spatial distribution of tree biomass in the region,taking into account exogenous and endogenous ecological processes,addressing random noise,and allowing the creation of dynamic maps for use by environmental managers.展开更多
基金partly supported by the National Natural Science Foundation of China(Grant No.61871241,No.61701221)the Natural Science Foundation of Jiangsu Province(No.BK20160781)+1 种基金Nantong Science and Technology Project(No.JC2018127,No.JC2019117)the Research Innovation Project for College Graduates of Jiangsu Province(No.KYLX16_0662)。
文摘The Poisson point process(PPP) has been widely used in wireless network modeling and performance analysis due to the independence between its nodes. Therefore, it may not be a suitable model for many of the exclusive networks between the nodes. This paper analyzes the energy efficiency(EE) and optimizes the two-tier heterogeneous cellular networks(Het Nets). Considering the mutual exclusion between macro base stations(MBSs) distribution, the deployment of MBSs is modeled by the Matérn hard-core point process(MHCPP), and the deployment of pico base stations(PBSs) is modeled by the PPP. We adopt a simple approximation method to study the signal to interference ratio(SIR) distribution in two-tier MHCPP-PPP networks and then derive the coverage probabilities, the average data rates and the energy efficiency of Het Nets. Finally, an optimization algorithm is proposed to improve the EE of Het Nets by controlling the transmit power of PBSs. The simulation results show that the EE of a system can be effectively improved by selecting the appropriate transmit power for the PBSs. In addition, two-tier MHCPP-PPP Het Nets have higher energy efficiency than two-tier PPP-PPP Het Nets.
基金the Ministry of Agriculture and Forestry key project“Puuta liikkeelle ja uusia tuotteita metsästä”(“Wood on the move and new products from forest”)Academy of Finland(project numbers 295100 , 306875).
文摘Background:The local pivotal method(LPM)utilizing auxiliary data in sample selection has recently been proposed as a sampling method for national forest inventories(NFIs).Its performance compared to simple random sampling(SRS)and LPM with geographical coordinates has produced promising results in simulation studies.In this simulation study we compared all these sampling methods to systematic sampling.The LPM samples were selected solely using the coordinates(LPMxy)or,in addition to that,auxiliary remote sensing-based forest variables(RS variables).We utilized field measurement data(NFI-field)and Multi-Source NFI(MS-NFI)maps as target data,and independent MS-NFI maps as auxiliary data.The designs were compared using relative efficiency(RE);a ratio of mean squared errors of the reference sampling design against the studied design.Applying a method in NFI also requires a proven estimator for the variance.Therefore,three different variance estimators were evaluated against the empirical variance of replications:1)an estimator corresponding to SRS;2)a Grafström-Schelin estimator repurposed for LPM;and 3)a Matérn estimator applied in the Finnish NFI for systematic sampling design.Results:The LPMxy was nearly comparable with the systematic design for the most target variables.The REs of the LPM designs utilizing auxiliary data compared to the systematic design varied between 0.74–1.18,according to the studied target variable.The SRS estimator for variance was expectedly the most biased and conservative estimator.Similarly,the Grafström-Schelin estimator gave overestimates in the case of LPMxy.When the RS variables were utilized as auxiliary data,the Grafström-Schelin estimates tended to underestimate the empirical variance.In systematic sampling the Matérn and Grafström-Schelin estimators performed for practical purposes equally.Conclusions:LPM optimized for a specific variable tended to be more efficient than systematic sampling,but all of the considered LPM designs were less efficient than the systematic sampling design for some target variables.The Grafström-Schelin estimator could be used as such with LPMxy or instead of the Matérn estimator in systematic sampling.Further studies of the variance estimators are needed if other auxiliary variables are to be used in LPM.
基金This work is funded in part by the Science and Technology Development Fund,Macao SAR(Grant Nos.0093/2022/A2,0076/2022/A2 and 0008/2022/AGJ)in part by the National Nature Science Foundation of China(Grant No.61872452)+3 种基金in part by Special fund for Dongguan’s Rural Revitalization Strategy in 2021(Grant No.20211800400102)in part by Dongguan Special Commissioner Project(Grant No.20211800500182)in part by Guangdong-Dongguan Joint Fund for Basic and Applied Research of Guangdong Province(Grant No.2020A1515110162)in part by University Special Fund of Guangdong Provincial Department of Education(Grant No.2022ZDZX1073).
文摘Interference management is one of the most important issues in the device-to-device(D2D)-enabled heterogeneous cellular networks(HetCNets)due to the coexistence of massive cellular and D2D devices in which D2D devices reuse the cellular spectrum.To alleviate the interference,an efficient interference management way is to set exclusion zones around the cellular receivers.In this paper,we adopt a stochastic geometry approach to analyze the outage probabilities of cellular and D2D users in the D2D-enabled HetCNets.The main difficulties contain three aspects:1)how to model the location randomness of base stations,cellular and D2D users in practical networks;2)how to capture the randomness and interrelation of cellular and D2D transmissions due to the existence of random exclusion zones;3)how to characterize the different types of interference and their impacts on the outage probabilities of cellular and D2D users.We then run extensive Monte-Carlo simulations which manifest that our theoretical model is very accurate.
文摘Background Accurate measurements of aboveground biomass(AGB)are essential for understanding the planet's carbon balance.The Atlantic Forest of the Serra do Mar in southeastern Brazil contains large areas of well-preserved remnants,characterized by mountainous terrain with significant orographic contrasts along its elevation gradient.This diverse landscape creates a variety of biophysical factors that strongly influence the spatial distribution of AGB.This study aims to estimate AGB using a hybrid geostatistical methodology,regression kriging simulation(RKS),to analyze AGB spatial distribution at a local scale(84 plots,each 0.01 ha)across a small forest fragment covering the entire tree-covered area(8777 ha).Building on traditional regression kriging method,this study introduces an innovative approach by incorporating Gaussian simulation to interpolate residuals,allowing RKS to account for uncertainties in the estimation process and create new results.This allows us to clearly distinguish exogenous ecological processes from endogenous ones before reaching the model's final estimate.Results Four regression kriging models were created,and the best-performing model used the Enhanced Vegetation Index and direct solar radiation(DSR),achieving an R^(2) of 55%.A Gaussian simulation was performed to interpolate the residuals of this model.The final results indicate that RKS provides accurate AGB estimates(RMSE=1.333 Mg/0.01 ha and R^(2) of 77%).Additionally,the inclusion of DSR as a new predictor variable enhances the precision of AGB estimates.The analysis showed that 63%of the sample pairs exhibited measurable spatial dependence.Conclusions Regression kriging simulation is proposed using Gaussian simulation,altering the classical application of regression kriging.For this,a case study was conducted in the Atlantic Forest of Serra do Mar to estimate the spatial distribution of tree biomass in a forest fragment of this region.We demonstrate that the proposed method better captures the heterogeneity of the region and produces more comprehensive results than regression kriging.Regression kriging simulation estimates tree biomass by considering the actual fluctuations of the spatial distribution of tree biomass in the region,taking into account exogenous and endogenous ecological processes,addressing random noise,and allowing the creation of dynamic maps for use by environmental managers.
