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
This paper proposes a wavelet based receiver structure for frequency-flat time-varying Rayleigh channels, consisting of a receiver front-end followed by a Maximum A-Posteriori (MAP) detector. Discretization of the rec...This paper proposes a wavelet based receiver structure for frequency-flat time-varying Rayleigh channels, consisting of a receiver front-end followed by a Maximum A-Posteriori (MAP) detector. Discretization of the received continuous time signal using filter banks is an essential stage in the front-end part, where the Fast Haar Transform (FHT) is used to reduce complexity. Analysis of our receiver over slow-fading channels shows that it is optimal for certain modulation schemes. By comparison with literature, it is shown that over such channels our receiver can achieve optimal performance for Time-Orthogonal modulation. Computed and Monte-Carlo simulated performance results over fast time-varying Rayleigh fading channels show that with Minimum Shift Keying (MSK), our receiver using four basis functions (filters) lowers the error floor by more than one order of magnitude with respect to other techniques of comparable complexity. Orthogonal Frequency Shift Keying (FSK) can achieve the same performance as Time-Orthogonal modulation for the slow-fading case, but suffers some degradation over fast-fading channels where it exhibits an error floor. Compared to MSK, however, Orthogonal FSK provides better performance.展开更多
为了获得性能高复杂度低的抗多径衰落接收机,该文将快速近似幂迭代(Fast approximated power iteration,FAPI)子空间跟踪算法应用到Rake接收机中,实现一种新型的自适应Rake接收机。采用Rake接收机来消除码分多址通信系统中多径衰落,利用...为了获得性能高复杂度低的抗多径衰落接收机,该文将快速近似幂迭代(Fast approximated power iteration,FAPI)子空间跟踪算法应用到Rake接收机中,实现一种新型的自适应Rake接收机。采用Rake接收机来消除码分多址通信系统中多径衰落,利用FAPI算法实现对信道参数的精准估计,从而进一步改善了Rake接收机的误码率性能。同时,由于FAPI算法的低复杂度,该文所提出的新型Rake接收机也具有复杂度低的特点。实验仿真结果表明:FAPI Rake接收机是一种高性能低复杂度的抗多径接收机。展开更多
The fifth generation(5G) network is expected to support significantly large amount of mobile data traffic and huge number of wireless connections,to achieve better spectrum- and energy-efficiency,as well as quality of...The fifth generation(5G) network is expected to support significantly large amount of mobile data traffic and huge number of wireless connections,to achieve better spectrum- and energy-efficiency,as well as quality of service(QoS) in terms of delay,reliability and security.Furthermore,the 5G network shall also incorporate high mobility requirements as an integral part,providing satisfactory service to users travelling at a speed up to 500 km/h.This paper provides a survey of potential high mobility wireless communication(HMWC) techniques for 5G network.After discussing the typical requirements and challenges of HMWC,key techniques to cope with the challenges are reviewed,including transmission techniques under the fast timevarying channels,network architecture with mobility support,and mobility management.Finally,future research directions on 5G high mobility communications are given.展开更多
Aiming at the severe inter-symbol interference and high bit error rate in short-wave fast time-varying channels,this paper designs a short-wave channel blind equalizer based on Convolution Neural Network(CNN),and anal...Aiming at the severe inter-symbol interference and high bit error rate in short-wave fast time-varying channels,this paper designs a short-wave channel blind equalizer based on Convolution Neural Network(CNN),and analyzes the influence of parameters in CNN structure on channel equalization,such as the number of convolution layers,the depth of convolution layer and the size of the convolution kernel layer.By simulating two typical short-wave time-varying channel,Rayleigh flat fading and frequency selective fading channels,we have the following results:1)Compared with the Recurrent Neural Network(RNN)structure equalizer,the CNN has higher accuracy during the training process,the convergence speed is faster,and the stability after convergence is higher.2)Under the condition of simulation,the CNN-based short-wave channel blind equalizer designed in this paper can effectively extract input signal when using 2×3×3 convolution kernel size and 2-layer convolutional layer.The characteristics of the classification layer improve the equalization performance while reducing the complexity of CNN structure.3)For the short-wave channel,the error rate of Convolution Neural Network Equalizer(CNNE)is lower than that of Recurrent Neural Network Equalizer(RNNE)under the same SNR.展开更多
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This paper proposes a wavelet based receiver structure for frequency-flat time-varying Rayleigh channels, consisting of a receiver front-end followed by a Maximum A-Posteriori (MAP) detector. Discretization of the received continuous time signal using filter banks is an essential stage in the front-end part, where the Fast Haar Transform (FHT) is used to reduce complexity. Analysis of our receiver over slow-fading channels shows that it is optimal for certain modulation schemes. By comparison with literature, it is shown that over such channels our receiver can achieve optimal performance for Time-Orthogonal modulation. Computed and Monte-Carlo simulated performance results over fast time-varying Rayleigh fading channels show that with Minimum Shift Keying (MSK), our receiver using four basis functions (filters) lowers the error floor by more than one order of magnitude with respect to other techniques of comparable complexity. Orthogonal Frequency Shift Keying (FSK) can achieve the same performance as Time-Orthogonal modulation for the slow-fading case, but suffers some degradation over fast-fading channels where it exhibits an error floor. Compared to MSK, however, Orthogonal FSK provides better performance.
文摘为了获得性能高复杂度低的抗多径衰落接收机,该文将快速近似幂迭代(Fast approximated power iteration,FAPI)子空间跟踪算法应用到Rake接收机中,实现一种新型的自适应Rake接收机。采用Rake接收机来消除码分多址通信系统中多径衰落,利用FAPI算法实现对信道参数的精准估计,从而进一步改善了Rake接收机的误码率性能。同时,由于FAPI算法的低复杂度,该文所提出的新型Rake接收机也具有复杂度低的特点。实验仿真结果表明:FAPI Rake接收机是一种高性能低复杂度的抗多径接收机。
基金supported by the National Basic Research Program of China (973 Program No.2012CB316100)
文摘The fifth generation(5G) network is expected to support significantly large amount of mobile data traffic and huge number of wireless connections,to achieve better spectrum- and energy-efficiency,as well as quality of service(QoS) in terms of delay,reliability and security.Furthermore,the 5G network shall also incorporate high mobility requirements as an integral part,providing satisfactory service to users travelling at a speed up to 500 km/h.This paper provides a survey of potential high mobility wireless communication(HMWC) techniques for 5G network.After discussing the typical requirements and challenges of HMWC,key techniques to cope with the challenges are reviewed,including transmission techniques under the fast timevarying channels,network architecture with mobility support,and mobility management.Finally,future research directions on 5G high mobility communications are given.
基金the National Natural Science Foundation of China(61671333)。
文摘Aiming at the severe inter-symbol interference and high bit error rate in short-wave fast time-varying channels,this paper designs a short-wave channel blind equalizer based on Convolution Neural Network(CNN),and analyzes the influence of parameters in CNN structure on channel equalization,such as the number of convolution layers,the depth of convolution layer and the size of the convolution kernel layer.By simulating two typical short-wave time-varying channel,Rayleigh flat fading and frequency selective fading channels,we have the following results:1)Compared with the Recurrent Neural Network(RNN)structure equalizer,the CNN has higher accuracy during the training process,the convergence speed is faster,and the stability after convergence is higher.2)Under the condition of simulation,the CNN-based short-wave channel blind equalizer designed in this paper can effectively extract input signal when using 2×3×3 convolution kernel size and 2-layer convolutional layer.The characteristics of the classification layer improve the equalization performance while reducing the complexity of CNN structure.3)For the short-wave channel,the error rate of Convolution Neural Network Equalizer(CNNE)is lower than that of Recurrent Neural Network Equalizer(RNNE)under the same SNR.
