This paper aimed to propose two algorithms,DA-M and RF-M,of reducing the impact of multipath interference(MPI)on intensity modulation direct detection(IM-DD)systems,particularly for four-level pulse amplitude modulati...This paper aimed to propose two algorithms,DA-M and RF-M,of reducing the impact of multipath interference(MPI)on intensity modulation direct detection(IM-DD)systems,particularly for four-level pulse amplitude modulation(PAM4)systems.DA-M reduced the fluctuation by averaging the signal in blocks,RF-M estimated MPI by subtracting the decision value of the corresponding block from the mean value of a signal block,and then generated interference-reduced samples by subtracting the interference signal from the product of the corresponding MPI estimate and then weighting factor.This paper firstly proposed to separate the signal before decision-making into multiple blocks,which significantly reduced the complexity of DA-M and RF-M.Simulation results showed that the MPI noise of 28 GBaud IMDD system under the linewidths of 1e5 Hz,1e6 Hz and 10e6 Hz can be effectively alleviated.展开更多
Communication opportunities among vehicles are important for data transmission over the Internet of Vehicles(IoV).Mixture models are appropriate to describe complex spatial-temporal data.By calculating the expectation...Communication opportunities among vehicles are important for data transmission over the Internet of Vehicles(IoV).Mixture models are appropriate to describe complex spatial-temporal data.By calculating the expectation of hidden variables in vehicle communication,Expectation Maximization(EM)algorithm solves the maximum likelihood estimation of parameters,and then obtains the mixture model of vehicle communication opportunities.However,the EM algorithm requires multiple iterations and each iteration needs to process all the data.Thus its computational complexity is high.A parameter estimation algorithm with low computational complexity based on Bin Count(BC)and Differential Evolution(DE)(PEBCDE)is proposed.It overcomes the disadvantages of the EM algorithm in solving mixture models for big data.In order to reduce the computational complexity of the mixture models in the IoV,massive data are divided into relatively few time intervals and then counted.According to these few counted values,the parameters of the mixture model are obtained by using DE algorithm.Through modeling and analysis of simulation data and instance data,the PEBCDE algorithm is verified and discussed from two aspects,i.e.,accuracy and efficiency.The numerical solution of the probability distribution parameters is obtained,which further provides a more detailed statistical model for the distribution of the opportunity interval of the IoV.展开更多
基金supported by the National Key Research and Development Program of China 2021YFB2900801the Young Elite Scientists Sponsorship Program of CIC 2021QNRC001+1 种基金National Natural Science Foundation of China NSFC,62201033,U22A2005the Foundation of the Beijing Engineering and Technology Center for Convergence Networks and Ubiquitous Services.
文摘This paper aimed to propose two algorithms,DA-M and RF-M,of reducing the impact of multipath interference(MPI)on intensity modulation direct detection(IM-DD)systems,particularly for four-level pulse amplitude modulation(PAM4)systems.DA-M reduced the fluctuation by averaging the signal in blocks,RF-M estimated MPI by subtracting the decision value of the corresponding block from the mean value of a signal block,and then generated interference-reduced samples by subtracting the interference signal from the product of the corresponding MPI estimate and then weighting factor.This paper firstly proposed to separate the signal before decision-making into multiple blocks,which significantly reduced the complexity of DA-M and RF-M.Simulation results showed that the MPI noise of 28 GBaud IMDD system under the linewidths of 1e5 Hz,1e6 Hz and 10e6 Hz can be effectively alleviated.
基金This work was supported by the Fundamental Research Funds for the Central Universities(Grant No.FRF-BD-20-11A)the Scientific and Technological Innovation Foundation of Shunde Graduate School,USTB(Grant No.BK19AF005).
文摘Communication opportunities among vehicles are important for data transmission over the Internet of Vehicles(IoV).Mixture models are appropriate to describe complex spatial-temporal data.By calculating the expectation of hidden variables in vehicle communication,Expectation Maximization(EM)algorithm solves the maximum likelihood estimation of parameters,and then obtains the mixture model of vehicle communication opportunities.However,the EM algorithm requires multiple iterations and each iteration needs to process all the data.Thus its computational complexity is high.A parameter estimation algorithm with low computational complexity based on Bin Count(BC)and Differential Evolution(DE)(PEBCDE)is proposed.It overcomes the disadvantages of the EM algorithm in solving mixture models for big data.In order to reduce the computational complexity of the mixture models in the IoV,massive data are divided into relatively few time intervals and then counted.According to these few counted values,the parameters of the mixture model are obtained by using DE algorithm.Through modeling and analysis of simulation data and instance data,the PEBCDE algorithm is verified and discussed from two aspects,i.e.,accuracy and efficiency.The numerical solution of the probability distribution parameters is obtained,which further provides a more detailed statistical model for the distribution of the opportunity interval of the IoV.