The challenges of severe Doppler effects in high-speed railway are considered. By building a cooperative antenna system; an algorithm of joint channel estimation and Doppler frequency offset （DFO） estimation is prop...The challenges of severe Doppler effects in high-speed railway are considered. By building a cooperative antenna system; an algorithm of joint channel estimation and Doppler frequency offset （DFO） estimation is proposed based on Ricean channel model. First, a maximum likelihood estimation （MLE） algorithm for DFO is designed, show- ing that the Doppler estimation can be obtained by estimating moving velocity of the train and the path loss with the exploitation of pilots that are placed inside the frame. Then a joint detection algorithm for the receiver is proposed to exploit multi-antenna diversity gains. Last, the theoretical Crammer Rao bound （CRB） for joint channel estimation and DFO estimation is derived. The steady performance of the system is confirmed by numerical simulations. In particular, when the Ricean fading channel parameter equals 5 and the velocities of train are 100 m/s and 150 m/s, the estimation variances of DFO are very close to the theoretical results obtained by using CRB. Meanwhile, the corresponding sig- nal to noise ratio loss is less than 1.5 dB when the bit error rate is 10-5 for 16QAM signals.展开更多
In the fifth-generation new radio(5G-NR) high-speed railway(HSR) downlink,a deep learning(DL) based Doppler frequency offset(DFO) estimation scheme is proposed by using the back propagation neural network(BPNN).The pr...In the fifth-generation new radio(5G-NR) high-speed railway(HSR) downlink,a deep learning(DL) based Doppler frequency offset(DFO) estimation scheme is proposed by using the back propagation neural network(BPNN).The proposed method mainly includes pre-training,training,and estimation phases,where the pre-training and training belong to the off-line stage,and the estimation is the online stage.To reduce the performance loss caused by the random initialization,the pre-training method is employed to acquire a desirable initialization,which is used as the initial parameters of the training phase.Moreover,the initial DFO estimation is used as input along with the received pilots to further improve the estimation accuracy.Different from the training phase,the initial DFO estimation in pre-training phase is obtained by the data and pilot symbols.Simulation results show that the mean squared error(MSE) performance of the proposed method is better than those of the available algorithms,and it has acceptable computational complexity.展开更多
To meet the access demands of massive terminal users,the space-based Internet of Things(IoT)requires sufficient frequency resources for allocation.However,the frequency resources that are currently available have alre...To meet the access demands of massive terminal users,the space-based Internet of Things(IoT)requires sufficient frequency resources for allocation.However,the frequency resources that are currently available have already been allocated to a great extent.Furthermore,the utilization rate of the allocated frequency resources is low.To support massive user access under restricted frequency resources,this work proposes a scheme based on Doppler frequency offset(DFO)pre-compensation to enhance spectrum utilization efficiency.By calculating the relative motion between the satellite and the transmitting terminal,combined with the length and transmission rate of the message,the optimal compensation value of the Doppler frequency deviation is determined.The frequencyprotection interval is reduced.Simulation results show that the pre-compensation method can expand the user access volume by 90–400 times.Properly selecting the number of message splits and transmission rate to perform DFO pre-compensation calculations can increase user access by an additional 45%or more.This method improves the spectrum utilization efficiency and provides a solution to the challenge of access by a large number of users.展开更多
基金supported by the China Major State Basic Research Development Program(973 Program,No.2012CB316100)National Natural Science Foundation of China(No.61171064)+2 种基金the China National Science and Technology Major Project(No.2010ZX03003-003)NSFC(No.61021001)the Open Research Fund of National Mobile Communications Research Laboratory,Southeast University(No.2011D13)
文摘The challenges of severe Doppler effects in high-speed railway are considered. By building a cooperative antenna system; an algorithm of joint channel estimation and Doppler frequency offset （DFO） estimation is proposed based on Ricean channel model. First, a maximum likelihood estimation （MLE） algorithm for DFO is designed, show- ing that the Doppler estimation can be obtained by estimating moving velocity of the train and the path loss with the exploitation of pilots that are placed inside the frame. Then a joint detection algorithm for the receiver is proposed to exploit multi-antenna diversity gains. Last, the theoretical Crammer Rao bound （CRB） for joint channel estimation and DFO estimation is derived. The steady performance of the system is confirmed by numerical simulations. In particular, when the Ricean fading channel parameter equals 5 and the velocities of train are 100 m/s and 150 m/s, the estimation variances of DFO are very close to the theoretical results obtained by using CRB. Meanwhile, the corresponding sig- nal to noise ratio loss is less than 1.5 dB when the bit error rate is 10-5 for 16QAM signals.
基金Supported by the National Science Foundation Program of Jiangsu Province(No.BK20191378)the National Science Research Project of Jiangsu Higher Education Institutions(No.18KJB510034)+1 种基金the 11th Batch of China Postdoctoral Science Fund Special Funding Project(No.2018T110530)the National Natural Science Foundation of China(No.61771255)。
文摘In the fifth-generation new radio(5G-NR) high-speed railway(HSR) downlink,a deep learning(DL) based Doppler frequency offset(DFO) estimation scheme is proposed by using the back propagation neural network(BPNN).The proposed method mainly includes pre-training,training,and estimation phases,where the pre-training and training belong to the off-line stage,and the estimation is the online stage.To reduce the performance loss caused by the random initialization,the pre-training method is employed to acquire a desirable initialization,which is used as the initial parameters of the training phase.Moreover,the initial DFO estimation is used as input along with the received pilots to further improve the estimation accuracy.Different from the training phase,the initial DFO estimation in pre-training phase is obtained by the data and pilot symbols.Simulation results show that the mean squared error(MSE) performance of the proposed method is better than those of the available algorithms,and it has acceptable computational complexity.
基金Project supported by the Proximity Space Science,Technology and Industry Guidance Fund(No.LKJJ-2023022-01)。
文摘To meet the access demands of massive terminal users,the space-based Internet of Things(IoT)requires sufficient frequency resources for allocation.However,the frequency resources that are currently available have already been allocated to a great extent.Furthermore,the utilization rate of the allocated frequency resources is low.To support massive user access under restricted frequency resources,this work proposes a scheme based on Doppler frequency offset(DFO)pre-compensation to enhance spectrum utilization efficiency.By calculating the relative motion between the satellite and the transmitting terminal,combined with the length and transmission rate of the message,the optimal compensation value of the Doppler frequency deviation is determined.The frequencyprotection interval is reduced.Simulation results show that the pre-compensation method can expand the user access volume by 90–400 times.Properly selecting the number of message splits and transmission rate to perform DFO pre-compensation calculations can increase user access by an additional 45%or more.This method improves the spectrum utilization efficiency and provides a solution to the challenge of access by a large number of users.
