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.

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.

Linear Precoder Design for Correlated Rician Fading Channel Under Imperfect CSI

In this paper,we have modeled a linear precoder for indoor multiuser multiple input multiple output(MU-MIMO)system with imperfect channel state information(CSI)at transmitter.The Rician channel is presumed to be mutually coupled and spatially,temporarily correlated.The imperfection with CSI is primarily due to the channel estimation error at receiver and feedback delay amidst the receiver and transmitter in CSI transmission.Along with,the insufficient spacing between the antenna at transmitter and receiver persuades mutual coupling(MC)among the array elements.In addition,the MIMO channel is presumed to be jointly correlated(Weichselberger correlation model).When we look back on the existing precoder design,it considered spatial correlation alone disregarding joint correlation of antenna array elements.With all above assumption,we have designed a linear precoder which minimizes mean squared error(MSE)subjected to total transmit power constraint for MUMIMO system.The simulation results proven that proposed precoder shows substantial enhancement in bit error rate(BER)performance in comparison with the existing technique.The mathematical analysis corroborates the simulation results.

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.

Influence of variable viscosity and double diffusion on the convective stability of a nanofluid flow in an inclined porous channel

The influence of variable viscosity and double diffusion on the convective stability of a nanofluid flow in an inclined porous channel is investigated.The DarcyBrinkman model is used to characterize the fluid flow dynamics in porous materials.The analytical solutions are obtained for the unidirectional and completely developed flow.Based on a normal mode analysis,the generalized eigenvalue problem under a perturbed state is solved.The eigenvalue problem is then solved by the spectral method.Finally,the critical Rayleigh number with the corresponding wavenumber is evaluated at the assigned values of the other flow-governing parameters.The results show that increasing the Darcy number,the Lewis number,the Dufour parameter,or the Soret parameter increases the stability of the system,whereas increasing the inclination angle of the channel destabilizes the flow.Besides,the flow is the most unstable when the channel is vertically oriented.

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.

Robust Stability and Stabilization of Discrete Singular Systems with Interval Time-varying Delay and Linear Fractional Uncertainty

The robust stability and robust stabilization problems for discrete singular systems with interval time-varying delay and linear fractional uncertainty are discussed. A new delay-dependent criterion is established for the nominal discrete singular delay systems to be regular, causal and stable by employing the linear matrix inequality （LMI） approach. It is shown that the newly proposed criterion can provide less conservative results than some existing ones. Then, with this criterion, the problems of robust stability and robust stabilization for uncertain discrete singular delay systems are solved, and the delay-dependent LMI conditions are obtained. Finally, numerical examples are given to illustrate the effectiveness of the proposed approach.

Global stabilizing controller design for linear time-varying systems and its application on BTT missiles

A parametric method for the gain-scheduled controller design of a linear time-varying system is given. According to the proposed scheduling method, the performance between adjacent characteristic points is preserved by the invariant eigenvalues and the gradually varying eigenvectors. A sufficient stability criterion is given by constructing a series of Lyapunov functions based on the selected discrete characteristic points. An important contribution is that it provides a simple and feasible approach for the design of gain-scheduled controllers for linear time-varying systems, which can guarantee both the global stability and the desired closed-loop performance of the resulted system. The method is applied to the design of a BTT missile autopilot and the simulation results show that the method is superior to the traditional one in sense of either global stability or system performance.

Optimal Iterative Learning Control for Batch Processes Based on Linear Time-varying Perturbation Model

A batch-to-batch optimal iterative learning control （ILC） strategy for the tracking control of product quality in batch processes is presented. The linear time-varying perturbation （LTVP） model is built for product quality around the nominal trajectories. To address problems of model-plant mismatches, model prediction errors in the previous batch run are added to the model predictions for the current batch run. Then tracking error transition models can be built, and the ILC law with direct error feedback is explicitly obtained, A rigorous theorem is proposed, to prove the convergence of tracking error under ILC, The proposed methodology is illustrated on a typical batch reactor and the results show that the performance of trajectory tracking is gradually improved by the ILC.

Delay-dependent stability and stabilization of a class of linear switched time-varying delay systems

The stability and stabilization of a class of linear switched time-varying delay systems are investigated. A piecewise quadratic Lyapunov function （PWQLF） is constructed and is used to obtain the stability conditions based on the linear matrix inequalities （LMIs）. The stabilizing controller for this class of system is then designed and the solution of the desired controller can be obtained by a cone complementary linearization algorithm. Numerical examples are provided to illustrate the less conservativeness of the new stability and the validity of the controller design procedures.

IDENTIFICATION OF TIME-VARYING MODAL PARAMETERS USING LINEAR TIME-FREQUENCY REPRESENTATION

A new method of parameter identification based on linear time-frequencyrepresentation and Hubert transform is proposed to identity modal parameters of linear time-varyingsystems from measured vibration responses. Using Gabor expansion and synthesis theory, measuredresponses are represented in the time-frequency domain and modal components are reconstructed bytime-frequency filtering. The Hilbert transform is applied to obtain time histories of the amplitudeand phase angle of each modal component, from which time-varying frequencies and damping ratios areidentified. The proposed method has been demonstrated with a numerical example in which a lineartime-varying system of two degrees of freedom is used to validate the identification scheme based ontime-frequency representation. Simulation results have indicated that time-frequency representationpresents an effective tool for modal parameter identification of time-varying systems.

Absolute Stability of Nonlinear Systems with Two Additive Time-varying Delay Components

In this paper, we present a new sufficient condition for absolute stability of Lure system with two additive time-varying delay components. This criterion is expressed as a set of linear matrix inequalities （LMIs）, which can be readily tested by using standard numerical software. We use this new criterion to stabilize a class of nonlinear time-delay systems. Some numerical examples are given to illustrate the applicability of the results using standard numerical software.

On designing memory state feedback controller for linear systems with interval time-varying delay

This paper is concerned with the design of a memory state feedback controller for linear systems with interval time-varying delays.The time delay is assumed to be a time-varying continuous function belonging to a given interval,which means that the lower and upper bounds of time-varying delay are available.First,a less conservative delay-range-dependent stability criteria is proposed by using a new interval fraction method.In the process of controller synthesis,the history information of system is considered in the controller design by introducing the lower delay state.Moreover,the usual memoryless state feedback controller for the underlying systems could be considered as a special case of the memory case.Finally,two numerical examples are given to show the effectiveness of the proposed method.

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.

Stability criteria for linear systems with multiple time-varying delays

New delay-independent and delay-dependent stability criteria for linear systems with multiple time-varying delays are established by using the time-domain method. The results are derived based on a new-type stability theorem for general retarded dynamical systems and new analysis techniques developed in the author's previous work. Unlike some results in the literature, all of the established results do not depend on the derivative of time-varying delays. Therefore, they are suitable for the case with very fast time-varying delays. In addition, some remarks are also given to explain the obtained results and to point out the limitations of the previous results in the literature. Keywords Stability - Delay-independent criteria - Delay-dependent criteria - Linear time-delay systems - Multiple time-varying delays This work was supported by NSFC Key-Project (No. 60334010) and Guangdong Province Natural Science Foundation of China (No. 31406).

Fault detection filter design for linear discrete time-varying systems with multiplicative noise

The problem of fault detection for linear discrete timevarying systems with multiplicative noise is dealt with.By using an observer-based robust fault detection filter（FDF） as a residual generator,the design of the FDF is formulated in the framework of H ∞ filtering for a class of stochastic time-varying systems.A sufficient condition for the existence of the FDF is derived in terms of a Riccati equation.The determination of the parameter matrices of the filter is converted into a quadratic optimization problem,and an analytical solution of the parameter matrices is obtained by solving the Riccati equation.Numerical examples are given to illustrate the effectiveness of the proposed method.

Two-Hop Gaussian Relay Channel with Linear Relaying： Achievable Rate and Optimization Design

The relay node with linear relaying transmits the linear combination of its past received signals.The optimization of two-hop relay channel with linear relaying is discussed in this paper.The capacity for the two-hop Gaussian relay channel with linear relaying is derived,which can be formulated as an optimization problem over the relaying matrix and the covariance matrix of the signals transmitted at the source.It is proved that the solution to this optimization problem is equivalent to a "single-letter" optimization problem.We also show that the solution to this "single-letter" optimization problem has the same form as the expression of the rate achieved by Time-Sharing Amplify and Forward(TSAF).In order to solve this equivalent problem,we proposed an iterative algorithm.Simulation results show that if channel gain of one hop is relatively smaller,the achievable rate with TSAF is closer to the max-flow min-cut capacity bound,but at a lower 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.