In this work,a frame work for time-varying channel modeling and simulation is proposed by using neural network(NN)to overcome the shortcomings in geometry based stochastic model(GBSM)and simulation approach.Two NN mod...In this work,a frame work for time-varying channel modeling and simulation is proposed by using neural network(NN)to overcome the shortcomings in geometry based stochastic model(GBSM)and simulation approach.Two NN models are developed for modeling of path loss together with shadow fading(SF)and joint small scale channel parameters.The NN models can predict path loss plus SF and small scale channel parameters accurately compared with measurement at 26 GHz performed in an outdoor microcell.The time-varying path loss and small scale channel parameters generated by the NN models are proposed to replace the empirical path loss and channel parameter random numbers in GBSM-based framework to playback the measured channel and match with its environment.Moreover,the sparse feature of clusters,delay and angular spread,channel capacity are investigated by a virtual array measurement at 28 GHz in a large waiting hall.展开更多
In this paper, a time-varying channel prediction method based on conditional generative adversarial network(CPcGAN) is proposed for time division duplexing/frequency division duplexing(TDD/FDD) systems. CPc GAN utiliz...In this paper, a time-varying channel prediction method based on conditional generative adversarial network(CPcGAN) is proposed for time division duplexing/frequency division duplexing(TDD/FDD) systems. CPc GAN utilizes a discriminator to calculate the divergence between the predicted downlink channel state information(CSI) and the real sample distributions under a conditional constraint that is previous uplink CSI. The generator of CPcGAN learns the function relationship between the conditional constraint and the predicted downlink CSI and reduces the divergence between predicted CSI and real CSI.The capability of CPcGAN fitting data distribution can capture the time-varying and multipath characteristics of the channel well. Considering the propagation characteristics of real channel, we further develop a channel prediction error indicator to determine whether the generator reaches the best state. Simulations show that the CPcGAN can obtain higher prediction accuracy and lower system bit error rate than the existing methods under the same user speeds.展开更多
针对当前广义频分复用(Generalized Frequency Division Multiplexing,GFDM)系统时变信道估计精度低的问题,提出基于稀疏贝叶斯学习的GFDM系统联合信道估计与符号检测算法.具体地,采用无干扰导频插入的GFDM多重响应信号模型,在稀疏贝叶...针对当前广义频分复用(Generalized Frequency Division Multiplexing,GFDM)系统时变信道估计精度低的问题,提出基于稀疏贝叶斯学习的GFDM系统联合信道估计与符号检测算法.具体地,采用无干扰导频插入的GFDM多重响应信号模型,在稀疏贝叶斯学习框架下,结合期望最大化算法(Expectation-Maximization,EM)和卡尔曼滤波与平滑算法实现块时变信道的最大似然估计;基于信道状态信息的估计值进行GFDM符号检测,并通过信道估计与符号检测的迭代处理逐步提高信道估计与符号检测的精度.仿真结果表明,所提算法能够获得接近完美信道状态信息条件下的误码率性能,且具有收敛速度快、对多普勒频移鲁棒性高等优点.展开更多
G3标准电力载波通信以正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)为核心抑制多径效应,结合RS(Reed-solomon)和交织编码等纠正随机错误,提高可靠性。由于信道环境时变性明显,上述措施不能满足高可靠通信需求。针对...G3标准电力载波通信以正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)为核心抑制多径效应,结合RS(Reed-solomon)和交织编码等纠正随机错误,提高可靠性。由于信道环境时变性明显,上述措施不能满足高可靠通信需求。针对这一问题,首先,提出了分段重复编码算法并应用于G3信号传输模型,通过对有效信息进行分段重复编码,增大信息通过低衰减信道的概率,提高可靠性;其次,设计了混合窗函数有限冲激响应(Finite Impulse Response,FIR)带通数字滤波算法对信号进行滤波处理,在保证相位的前提下,尽可能去除带外噪声干扰,优化OFDM的性能,并提高分段重复编码的作用。经性能测试,所形成的分段重复编码和FIR数字滤波的传输模型在平均误码率为10-2时有2～3 d B的性能提升,适合在强时变性和强噪声的信道中进行高可靠的通信传输。展开更多
The accuracy of acquired channel state information(CSI)for beamforming design is essential for achievable performance in multiple-input multiple-output(MIMO)systems.However,in a high-speed moving scene with time-divis...The accuracy of acquired channel state information(CSI)for beamforming design is essential for achievable performance in multiple-input multiple-output(MIMO)systems.However,in a high-speed moving scene with time-division duplex(TDD)mode,the acquired CSI depending on the channel reciprocity is inevitably outdated,leading to outdated beamforming design and then performance degradation.In this paper,a robust beamforming design under channel prediction errors is proposed for a time-varying MIMO system to combat the degradation further,based on the channel prediction technique.Specifically,the statistical characteristics of historical channel prediction errors are exploited and modeled.Moreover,to deal with random error terms,deterministic equivalents are adopted to further explore potential beamforming gain through the statistical information and ultimately derive the robust design aiming at maximizing weighted sum-rate performance.Simulation results show that the proposed beamforming design can maintain outperformance during the downlink transmission time even when channels vary fast,compared with the traditional beamforming design.展开更多
针对时变信道环境下传统信道估计方法性能受限,其他基于深度学习的信道估计方法估计精度低或复杂度高的问题,提出一种基于长短期记忆结构的信道估计网络,由双向长短期记忆(bidirectional long short-term memory,BiLSTM)网络和多层感知...针对时变信道环境下传统信道估计方法性能受限,其他基于深度学习的信道估计方法估计精度低或复杂度高的问题,提出一种基于长短期记忆结构的信道估计网络,由双向长短期记忆(bidirectional long short-term memory,BiLSTM)网络和多层感知器(multilayer perceptron,MLP)网络组成,即BiLSTM-MLP.首先,利用BiLSTM网络来学习信道的时变特性;然后,利用MLP网络进行去噪并重构信道估计.仿真结果表明,所提出的信道估计方法与传统方法相比,性能提升明显,与同类型的基于深度学习的估计方法相比,复杂度较低且性能更优.此外,所提方法还具有对不同导频密度的鲁棒性.展开更多
Filter bank multicarrier(FBMC)systems with offset quadrature amplitude modulation(OQAM)need long data blocks to achieve high spectral efficiency.However,the transmission of long data blocks in underwater acoustic(UWA)...Filter bank multicarrier(FBMC)systems with offset quadrature amplitude modulation(OQAM)need long data blocks to achieve high spectral efficiency.However,the transmission of long data blocks in underwater acoustic(UWA)communication systems often encounters the challenge of time-varying channels.This paper proposes a time-varying channel tracking method for short-range high-rate UWA FBMC-OQAM communication applications.First,a known preamble is used to initialize the channel estimation at the initial time of the signal block.Next,the estimated channel is applied to detect data symbols at several symbol periods.The detected data symbols are then reused as new pilots to estimate the next time channel.In the above steps,the unified transmission matrix model is extended to describe the time-varying channel input-output model in this paper and is used for symbol detection.Simulation results show that the channel tracking error can be reduced to less than−20 dB when the channel temporal coherence coefficient exceeds 0.75 within one block period of FBMC-OQAM signals.Compared with conventional known-pilot-based methods,the proposed method needs lower system overhead while exhibiting similar time-varying channel tracking performance.The sea trial results further proved the practicability of the proposed method.展开更多
Traditional antenna calibration methods for time division duplex （TDD） systems asSume that the flee-space channel remains the same during calibration, which is unreasonable under the high-speed rail and other time-v...Traditional antenna calibration methods for time division duplex （TDD） systems asSume that the flee-space channel remains the same during calibration, which is unreasonable under the high-speed rail and other time-varying channel scenarios, and will cause calibration error due to time variability. This paper proposes an antenna calibration method for time-varying channels. In the proposed method, the transceiver first sequentially sends a pilot signal to ob- tain equivalent do^vnlink and uplink channel responses. Then, by predicting the downlink （uplink） channel response fed back from the receiver using the channel prediction algorithm, the transmitter obtains the channel response correspond- ing to the channel response on uplink （downlink）. Finally, the transmitter calculates the transmission calibration factor through the prediction value. Compared with the traditional antenna calibration method, this method can improve the accuracy of the calibration factor. Simulation results show that the performance degradation of antenna calibration can be caused by time-varying channels and the proposed method can well compensate for the performance loss and sig- nificantly improve the antenna calibration performance for time-varying channels.展开更多
基金supported by the National Nature Science Foundation of China(NSFC)under grant No.61771194supported by Key Program of Beijing Municipal Natural Science Foundation with No.17L20052
文摘In this work,a frame work for time-varying channel modeling and simulation is proposed by using neural network(NN)to overcome the shortcomings in geometry based stochastic model(GBSM)and simulation approach.Two NN models are developed for modeling of path loss together with shadow fading(SF)and joint small scale channel parameters.The NN models can predict path loss plus SF and small scale channel parameters accurately compared with measurement at 26 GHz performed in an outdoor microcell.The time-varying path loss and small scale channel parameters generated by the NN models are proposed to replace the empirical path loss and channel parameter random numbers in GBSM-based framework to playback the measured channel and match with its environment.Moreover,the sparse feature of clusters,delay and angular spread,channel capacity are investigated by a virtual array measurement at 28 GHz in a large waiting hall.
基金supported in part by the National Science Fund for Distinguished Young Scholars under Grant 61925102in part by the National Natural Science Foundation of China(62201087&92167202&62101069&62201086)in part by the Beijing University of Posts and Telecommunications-China Mobile Research Institute Joint Innovation Center。
文摘In this paper, a time-varying channel prediction method based on conditional generative adversarial network(CPcGAN) is proposed for time division duplexing/frequency division duplexing(TDD/FDD) systems. CPc GAN utilizes a discriminator to calculate the divergence between the predicted downlink channel state information(CSI) and the real sample distributions under a conditional constraint that is previous uplink CSI. The generator of CPcGAN learns the function relationship between the conditional constraint and the predicted downlink CSI and reduces the divergence between predicted CSI and real CSI.The capability of CPcGAN fitting data distribution can capture the time-varying and multipath characteristics of the channel well. Considering the propagation characteristics of real channel, we further develop a channel prediction error indicator to determine whether the generator reaches the best state. Simulations show that the CPcGAN can obtain higher prediction accuracy and lower system bit error rate than the existing methods under the same user speeds.
文摘针对当前广义频分复用(Generalized Frequency Division Multiplexing,GFDM)系统时变信道估计精度低的问题,提出基于稀疏贝叶斯学习的GFDM系统联合信道估计与符号检测算法.具体地,采用无干扰导频插入的GFDM多重响应信号模型,在稀疏贝叶斯学习框架下,结合期望最大化算法(Expectation-Maximization,EM)和卡尔曼滤波与平滑算法实现块时变信道的最大似然估计;基于信道状态信息的估计值进行GFDM符号检测,并通过信道估计与符号检测的迭代处理逐步提高信道估计与符号检测的精度.仿真结果表明,所提算法能够获得接近完美信道状态信息条件下的误码率性能,且具有收敛速度快、对多普勒频移鲁棒性高等优点.
文摘G3标准电力载波通信以正交频分复用(Orthogonal Frequency Division Multiplexing,OFDM)为核心抑制多径效应,结合RS(Reed-solomon)和交织编码等纠正随机错误,提高可靠性。由于信道环境时变性明显,上述措施不能满足高可靠通信需求。针对这一问题,首先,提出了分段重复编码算法并应用于G3信号传输模型,通过对有效信息进行分段重复编码,增大信息通过低衰减信道的概率,提高可靠性;其次,设计了混合窗函数有限冲激响应(Finite Impulse Response,FIR)带通数字滤波算法对信号进行滤波处理,在保证相位的前提下,尽可能去除带外噪声干扰,优化OFDM的性能,并提高分段重复编码的作用。经性能测试,所形成的分段重复编码和FIR数字滤波的传输模型在平均误码率为10-2时有2～3 d B的性能提升,适合在强时变性和强噪声的信道中进行高可靠的通信传输。
基金supported by the ZTE Industry⁃University⁃Institute Cooper⁃ation Funds under Grant No.2021ZTE01⁃03.
文摘The accuracy of acquired channel state information(CSI)for beamforming design is essential for achievable performance in multiple-input multiple-output(MIMO)systems.However,in a high-speed moving scene with time-division duplex(TDD)mode,the acquired CSI depending on the channel reciprocity is inevitably outdated,leading to outdated beamforming design and then performance degradation.In this paper,a robust beamforming design under channel prediction errors is proposed for a time-varying MIMO system to combat the degradation further,based on the channel prediction technique.Specifically,the statistical characteristics of historical channel prediction errors are exploited and modeled.Moreover,to deal with random error terms,deterministic equivalents are adopted to further explore potential beamforming gain through the statistical information and ultimately derive the robust design aiming at maximizing weighted sum-rate performance.Simulation results show that the proposed beamforming design can maintain outperformance during the downlink transmission time even when channels vary fast,compared with the traditional beamforming design.
文摘针对时变信道环境下传统信道估计方法性能受限,其他基于深度学习的信道估计方法估计精度低或复杂度高的问题,提出一种基于长短期记忆结构的信道估计网络,由双向长短期记忆(bidirectional long short-term memory,BiLSTM)网络和多层感知器(multilayer perceptron,MLP)网络组成,即BiLSTM-MLP.首先,利用BiLSTM网络来学习信道的时变特性;然后,利用MLP网络进行去噪并重构信道估计.仿真结果表明,所提出的信道估计方法与传统方法相比,性能提升明显,与同类型的基于深度学习的估计方法相比,复杂度较低且性能更优.此外,所提方法还具有对不同导频密度的鲁棒性.
基金Supported by the National Natural Science Foundation of China under Grant Nos.62171405,62225114 and 62101489.
文摘Filter bank multicarrier(FBMC)systems with offset quadrature amplitude modulation(OQAM)need long data blocks to achieve high spectral efficiency.However,the transmission of long data blocks in underwater acoustic(UWA)communication systems often encounters the challenge of time-varying channels.This paper proposes a time-varying channel tracking method for short-range high-rate UWA FBMC-OQAM communication applications.First,a known preamble is used to initialize the channel estimation at the initial time of the signal block.Next,the estimated channel is applied to detect data symbols at several symbol periods.The detected data symbols are then reused as new pilots to estimate the next time channel.In the above steps,the unified transmission matrix model is extended to describe the time-varying channel input-output model in this paper and is used for symbol detection.Simulation results show that the channel tracking error can be reduced to less than−20 dB when the channel temporal coherence coefficient exceeds 0.75 within one block period of FBMC-OQAM signals.Compared with conventional known-pilot-based methods,the proposed method needs lower system overhead while exhibiting similar time-varying channel tracking performance.The sea trial results further proved the practicability of the proposed method.
基金supported by the National Natural Science Foundation of China(Nos.61032002,61101090 and 60902026)Chinese Important National Science & Technology Specific Projects(No.2011ZX03001-007-01)
文摘Traditional antenna calibration methods for time division duplex （TDD） systems asSume that the flee-space channel remains the same during calibration, which is unreasonable under the high-speed rail and other time-varying channel scenarios, and will cause calibration error due to time variability. This paper proposes an antenna calibration method for time-varying channels. In the proposed method, the transceiver first sequentially sends a pilot signal to ob- tain equivalent do^vnlink and uplink channel responses. Then, by predicting the downlink （uplink） channel response fed back from the receiver using the channel prediction algorithm, the transmitter obtains the channel response correspond- ing to the channel response on uplink （downlink）. Finally, the transmitter calculates the transmission calibration factor through the prediction value. Compared with the traditional antenna calibration method, this method can improve the accuracy of the calibration factor. Simulation results show that the performance degradation of antenna calibration can be caused by time-varying channels and the proposed method can well compensate for the performance loss and sig- nificantly improve the antenna calibration performance for time-varying channels.
