Fragmental Weight-Conservation Combining Scheme for Statistical Signal Transmissions under Fast Time-Varying Channels

Statistical Signal Transmission(SST)is a technique based on orthogonal frequency-division multiplexing(OFDM)and adopts cyclostationary features,which can transmit extra information without additional bandwidth.However,the more complicated environment in 5G communication systems,especially the fast time-varying scenarios,will dramatically degrade the performance of the SST.In this paper,we propose a fragmental weight-conservation combining(FWCC)scheme for SST,to overcome its performance degradation under fast time-varying channels.The proposed FWCC scheme consists of three phases:1、incise the received OFDM stream into pieces;2、endue different weights for fine and contaminated pieces,respectively;3、combine cyclic autocorrelation function energies of all the pieces;and 4、compute the final feature and demodulate data of SST.Through these procedures above,the detection accuracy of SST will be theoretically refined under fast time-varying channels.Such an inference is confirmed through numerical results in this paper.It is demonstrated that the BER performance of proposed scheme outperforms that of the original scheme both in ideal channel estimation conditions and in imperfect channel estimation conditions.In addition,we also find the experiential optimal weight distribution strategy for the proposed FWCC scheme,which facilitates practical applications.

Adversarial Training-Aided Time-Varying Channel Prediction for TDD/FDD Systems

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.

Robust Beamforming Under Channel Prediction Errors for Time-Varying MIMO System

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.

Tensor Decomposition-Based Channel Estimation and Sensing for Millimeter Wave MIMO-OFDM V2I Systems

An integrated sensing and communication(ISAC)scheme for a millimeter wave(mmWave)multiple-input multiple-output orthogonal frequency division multiplexing(MIMO-OFDM)Vehicle-to-Infrastructure(V2I)system is presented,in which both the access point(AP)and the vehicle are equipped with large antenna arrays and employ hybrid analog and digital beamforming structures to compensate the path loss,meanwhile compromise between hardware complexity and system performance.Based on the sparse scattering nature of the mmWave channel,the received signal at the AP is organized to a four-order tensor by the introduced novel frame structure.A CANDECOMP/PARAFAC(CP)decomposition-based method is proposed for time-varying channel parameter extraction,including angles of departure/arrival(AoDs/AoAs),Doppler shift,time delay and path gain.Then leveraging the estimates of channel parameters,a nonlinear weighted least-square problem is proposed to recover the location accurately,heading and velocity of vehicles.Simulation results show that the proposed methods are effective and efficient in time-varying channel estimation and vehicle sensing in mmWave MIMOOFDM V2I systems.

Neural Network and GBSM Based Time-Varying and Stochastic Channel Modeling for 5G Millimeter Wave Communications

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.

Compressed sensing based channel estimation for fast fading OFDM systems

A compressed sensing（CS） based channel estimation algorithm is proposed by using the delay-Doppler sparsity of the fast fading channel.A compressive basis expansion channel model with sparsity in both time and frequency domains is given.The pilots in accordance with a novel random pilot matrix in both time and frequency domains are sent to measure the delay-Doppler sparsity channel.The relatively nonzero channel coefficients are tracked by random pilots at a sampling rate significantly below the Nyquist rate.The sparsity channels are estimated from a very limited number of channel measurements by the basis pursuit algorithm.The proposed algorithm can effectively improve the channel estimation performance when the number of pilot symbols is reduced with improvement of throughput efficiency.

SUBSPACE METHOD FOR BLIND IDENTIFICATION OF CDMA TIME-VARYING CHANNELS

A new blind method is proposed for identification of CDMA Time-Varying (TV)channels in this paper. By representing the TV channel's impulse responses in the delay-Doppler spread domain, the discrete-time canonical model of CDMA-TV systems is developed and a subspace method to identify blindly the Time-Invariant (TI) coordinates is proposed. Unlike existing basis expansion methods, this new algorithm does not require .estimation of the base frequencies, neither need the assumption of linearly varying delays across symbols. The algorithm offers definite explanation of the expansion coordinates. Simulation demonstrates the effectiveness of the algorithm.

Antenna calibration using channel prediction for time-varying channels

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.

Digital Channelization Technology for HF Communication Base on Fast Filter Bank

Channelization typically realized by digital filter banks is an important topic in high frequency(HF) communication and software defined radios(SDR) areas. Channelization has a rigorous requirement for the characteristic of frequency response, e.g., steep transitional band and sharp decay. To address this issue, we investigated the feasibility and implementation of applying fast filter bank(FFB) in channelization in this paper. We analyzed the butterfly structure of FFB similar with fast Fourier transform(FFT), in which prototype sub-filters are cascaded to achieve a low complexity. Hence, it is suitable for designing filter bank with steep transitional band and sharp decay in stop-band. Moreover, we designed a pipelined structure of FFB to achieve a balance between area and performance. Design example shows that FFB has lower computational complexity compared with the other filter banks.

Compressed sensing based channel estimation using group random unit pilot for OFDM systems in fast moving environment~

A compressed sensing （CS） based channel estimation algorithm is proposed in the fast moving environment. A sparse basis expansion channel model in both time and frequency domain is given.Pilots are placed according to a novel random unit pilot matrix （RUPM） to measure the delay- Doppler sparse channel. The sparse channels are recovered by an extension group orthogonal matching pursuit （GOMP） algorithm, enjoying the diversity gain from multi-symbol processing. The relatively nonzero channel coefficients are estimated from a very limited number of pilots at a sampling rate significantly below the Nyquist rate. The simulation results show that the new channel estimator can provide a considerable performance improvement for the fast fading channels. Three significant reductions are achieved in the required number of pilots, memory requirements and computational complexity.

Deep learning-based time-varying channel estimation with basis expansion model for MIMO-OFDM system

For high-speed mobile MIMO-OFDM system,a low-complexity deep learning(DL) based timevarying channel estimation scheme is proposed.To reduce the number of estimated parameters,the basis expansion model(BEM) is employed to model the time-varying channel,which converts the channel estimation into the estimation of the basis coefficient.Specifically,the initial basis coefficients are firstly used to train the neural network in an offline manner,and then the high-precision channel estimation can be obtained by small number of inputs.Moreover,the linear minimum mean square error(LMMSE) estimated channel is considered for the loss function in training phase,which makes the proposed method more practical.Simulation results show that the proposed method has a better performance and lower computational complexity compared with the available schemes,and it is robust to the fast time-varying channel in the high-speed mobile scenarios.

Pilot Placement for Time-Varying MIMO OFDM Channels with Virtual Subcarriers

The rapid time-variation of a fading multipath environment can impair the performance of multiple-input multiple-output orthogonal frequency division multiplexing (MIMO OFDM). This paper proposes a pilot placement method for MIMO OFDM systems under time-varying channels with the guard band. The time-varying channel is described by complex exponential basis expansion model (BEM). We discuss the least square (LS) channel estimation to obtain the minimum mean square error (MSE) and derive the pilot allocation that can satisfy the minimum MSE with regard to guard band in time-varying channels. It is shown that optimal pilot clusters can distribute non-uniformly in frequency domain and minimize the MSE. We generalize our scheme over G OFDM symbols and compare it with comb pilots. It is demonstrated that the proposed approach is more effective than previous work. Simulation results validate our theoretical analysis.

Multiple Vandermonde Decomposition Based Channel Estimation for Mm Wave MIMO High-Mobility Communication in 5G and Beyond

Millimeter wave(mmWave)massive massive multiple input multiple output(MIMO)technique has been regarded as the viable solution for vehicular communications in 5G and beyond.To achieve the substantial increase in date rates,it is important to take an effective channel state information(CSI).However,existing channel estimation strategies are unavailable since the users high-mobility.To solve above issues,in this paper,inspired by a specific antenna structure,we propose a novel approach for fast time-varying channel estimation.Specifically,by considering the vehicle scenario with high-mobility,a corresponding mathematical model is firstly established.Then,based on the special structural of the sparse array,the switch network is used to replace the convention phase shifter of mmWave hybrid system,which can effectively reduce the number of radio-frequency(RF)chains and antennas.Furthermore,by solving the semidefinite programming(SDP)duality problem,the Doppler frequency and path parameters are effectively estimated.Simulation results are shown that the computational complexity and estimation accuracy of the proposed algorithm is superior than that of the traditional schemes.

Sparse Recovery of Linear Time-Varying Channel in OFDM System

In order to improve the performance of linear time-varying（LTV）channel estimation,based on the sparsity of channel taps in time domain,a sparse recovery method of LTV channel in orthogonal frequency division multiplexing（OFDM）system is proposed.Firstly,based on the compressive sensing theory,the average of the channel taps over one symbol duration in the LTV channel model is estimated.Secondly,in order to deal with the inter-carrier interference（ICI）,the group-pilot design criterion is used based on the minimization of mutual coherence of the measurement.Finally,an efficient pilot pattern optimization algorithm is proposed by a dual layer loops iteration.The simulation results show that the new method uses less pilots,has a smaller bit error ratio（BER）,and greater ability to deal with Doppler frequency shift than the traditional method does.

A Receiver Structure for Frequency-Flat Time-Varying Rayleigh Channels and Performance Analysis

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.

Methods to Improve the Long Distance Time-Varying Channel Transmission Performance of Expendable Profiler

To improve the transmission performance of XCTD channel, this paper proposes a method to measure directly and fit the channel transmission characteristics by using frequency sweeping method. Sinusoidal signals with a frequency range of 100 Hz to 10 k Hz and an interval of 100 Hz are used to measure transmission characteristics of channels with lengths of 300 m, 800 m, 1300 m, and 1800 m. The correctness of the fitted channel characteristics by transmitting square wave, composite waves of different frequencies, and ASK modulation are verified. The results show that when the frequency of the signal is below 1500 Hz, the channel has very little effect on the signal. The signal compensated for amplitude and phase at the receiver is not as good as the uncompensated signal.Alternatively, when the signal frequency is above 1500 Hz, the channel distorts the signal. The quality of signal compensated for amplitude and phase at receiver is better than that of the uncompensated signal. Thus, we can select the appropriate frequency for XCTD system and the appropriate way to process the received signals. Signals below1500 Hz can be directly used at the receiving end. Signals above 1500 Hz are used after amplitude and phase compensation at the receiving end.

Novel differential unitary space-time modulation schemes for fast fading channels

Differential unitary space-time modulation （DUSTM）, which obtains full transmit diversity in slowly fiat-fading channels without channel state iufonnation, has generated significant interests recently. To combat frequency-selective fading, DUSTM has been applied to each subcarrier of an OFDM system and DUSTM-OFDM system was proposed. Both DUSTM and DUSTM-OFDM, however, are designed for slowly fading channels and suffer performance deterioration in fast fading channels. In this paper, two novel differential unitary space-time modulation schemes are proposed for fast fading channels. For fast fiat-fading channels, a subatrix interleaved DUSTM （SMI-DUSTM） scheme is proposed, in which matrix-segmentation and sub-matrix based interleaving are introduced into DUSTM system. For fast frequency-selective fading channels, a differential unitary space-frequency modulation （DUSFM） scheme is proposed, in which existing unitary space-time codes are employed across transmit antennas and OFDM subcarriers simultaneouslv and differential modulation is performed between two adjacent OFDM blocks. Compared with DUSTM and DUSTM-OFDM schemes, SMI-DUSTM and DUSFM-OFDM are more robust to fast channel fading with low decoding complexity, which is demonstrated by performance analysis and simulation resuits.

液态金属冷却快堆子通道分析软件SACOS-LMR研发与工程应用

子通道分析方法是反应堆堆芯设计和热工水力分析的重要手段之一,对于我国提出的压水堆-快堆-聚变堆三步走核能发展战略,开发适用于液态金属冷却快堆热工安全分析的子通道分析程序具有重要意义。本文基于西安交通大学热工水力研究室自主开发的压水堆子通道程序SACOS,通过添加液态金属快堆特有的模型,如绕丝模型、盒间流模型、液态金属对流换热模型等,扩展至适用于液态金属快堆的子通道分析程序SACOS-LMR,该程序具备对液态金属快堆组件开展稳态和瞬态热工水力分析的功能。结合卡尔斯鲁厄开展的37棒钠冷瞬态实验,完成了SACOS-LMR程序的瞬态功能验证。基于验证后的SACOS-LMR程序,对欧洲铅冷快堆(ALFRED)堆芯开展了稳态工况和瞬态事故工况下的热工安全特性分析,计算结果合理,且与同类程序保持一致,表明SACOS-LMR程序可用于液态金属快堆的堆芯设计和热工水力分析研究。

一种相控阵通道的快速测量方法

随着相控阵系统在通信领域的大规模应用,需要供应商提供能够长期稳定工作的设备。而相控阵通道幅相特性在使用的过程中会随着时间产生变化,最终会导致波束性能下降。为了简化相控阵后期维护,降低维护时间和成本,需要在其维护阶段对相控阵通道进行幅相特性测量。这种测量必须由相控阵系统独立完成,且不应该依赖外部环境。传统的测量方法是依次对每个通道进行独立的测量,这种测量方法效率低下,大规模相控阵的测量时间一般都在数十分钟以上,会使通信业务长时间中断,不利于系统快速维护的需求。目前,对相控阵通道幅相的快速测量方法主要是在相控阵天线位于特定的测试环境下进行,目的是加快相控阵的生产周期,不适用于后期维护。将多载波和系统同步结合,提出了一种相控阵通道的快速测量方法。该方法在相同测量精度下,测量所消耗的时间大约比传统串行测量方法少两个数量级,相比于已有的快速测量方法测量时间大幅缩短。最后通过仿真验证了方法的有效性,得出了测量精度和测量时间的关系。并在相同测量精度条件下与传统串行测量方法和已有快速测量方法的测量时间进行对比。

南水北调中线渠首前馈控制方案快速计算分析

明渠调水工程中为适时适量的满足下游用水需求,需要提前制定控制方案。前馈控制方案的设计往往较为复杂,且影响因素众多,调度人员难以快速做出决策。基于蓄量补偿法对南水北调中线工程刁河节制闸至十二里河节制闸段作前馈控制,基于蓄量补偿计算结果,分析了初始流量、分水流量与前馈控制时间的关系,构建了单、双变量的前馈控制时间快速计算公式。探究了不同因素对蓄量补偿控制下游水位偏差的影响,得到初始流量、分水流量与下游水位最大偏差之间的函数关系式。针对工程运行中的极端工况,原蓄量补偿控制的下游水位偏差过大的问题,采用二次蓄量补偿规则对渠池进行前馈控制,有效减小了下游水位偏差。