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Long short-term memory(LSTM) is an effective solution to time sequence prediction. Considering the data perturbations, in this letter, a variant model of LSTM is proposed to achieve robustness of prediction. Specifically, data processing procedure in the recurrent unit of proposed model is reformulated, the gates are controlled by only one variable, and the variable is the sum of long-term memory and the current input. Due to the simplified two-gate structure of proposed model, the speed of prediction is improved as well. The experiments on three datasets verify that the proposed model with simplified structure has higher robustness and shorter running time than the traditional LSTM model.

This paper applies the repetition index scheme (RIS) to the channel identification of cyclic prefixed (CP) multiple-input Multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems with virtual carriers (VCs) in the environment of the number of receive antennas being no less than that of transmit antennas. The VCs will cause a rank deficiency problem in computing the subspace information. With the subcarrier mapping matrix, the received signal is simplified to remove the rank deficiency. We use the RIS scheme to generate many times of equivalent symbols so the channel identification can converge with few received OFDM blocks. The RIS scheme will convert the white noise into non-white noise. With the Cholesky factorization, a noise whitening technique is developed to turn the non-white noise back to white noise. We further analyze the necessary conditions of identifiability of channel estimation. Simulations are performed to show the superiority of the proposed method.

This paper studies a downlink massive multiuser multiple-input multiple-output system with PSK modulation, where the base station employs 1-bit digital-to-analog converters (DACs). We first exploit the concept of constructive interference (CI) to express the received PSK signal in terms of the coordinate vector. With the coordinate vector, we construct a nonlinear mapping (NM) function in which the function value is small when the coordinate coefficient is large. Then we present a low-complexity NM-MinSum algorithm to solve a single-objective optimization problem which minimizes the sum of the NM function of the coordinate vector. Further, we propose a two-objective optimization problem and develop the NM-DualMax algorithm to find its solution. The numerical results show the proposed algorithms achieve comparable performance to the existing nonlinear precoding approaches.

This paper investigates the subspace channel identification for multiple-input multiple-output zero-padded orthogonal frequency-division multiplexing systems with space–time block code and virtual carriers (VCs). We first develop a new subspace channel identification model when the VCs exist. Then, two schemes, the forward–backward method and the repetition index scheme (RIS), are developed to generate many times equivalent symbols and then to enhance the performance of the subspace channel identification. With these methods, more accurate subspace channel identification can be obtained by using only a few received blocks. Further, the noise pre-whitening technique is investigated to decrease the nonwhite noise effect caused by the RIS scheme. We also provide complexity analyses of the proposed methods. With the acceptable computational cost, the proposed methods significantly improve the performances of the channel identification and the equalization. Simulations are carried out to demonstrate the effectiveness of the proposed methods.

Fast blind subspace channel estimation using circular property of the channel matrix is investigated for space–time block coded (STBC) multiple-input multiple-output orthogonal frequency division multiplexing (MIMO–OFDM) systems in this paper. The noise subspace computed from the correlation matrix of received signals requires a large number of symbols to converge in the subspace channel estimation. Using the circular property of the channel matrix, we propose both the cyclic repetition method (CRM) and the forward–backward method (FBM) to generate N times of equivalent signals for each STBC–OFDM symbol, respectively, where N is the size of FFT operation. With these equivalent symbols, the proposed CRM, FBM and CRM–FBM (CFBM) channel estimations can perform very well within a few OFDM symbols. The CRM, FBM and CFBM schemes are applicable to the CP-OFDM, ZP-OFDM and VC-OFDM systems, respectively. The identifiability of the subspace channel estimation is investigated that the channel matrix is determined up to two ambiguity matrices. Computer simulations demonstrate that the CRM-based, FBM-based and CFBM-based channel estimations have better performances than the conventional ones.

In the paper, the problem on image encryption on the basis of a chaotic system with time delay is addressed. Firstly a kind of time delay chaotic system is proposed, which has the different dynamical behavior over time delay. Then based on the time delay of the new system, a novel approach is designed to achieve the image encryption. Final experiments illuminate that the given image encryption method have good effectiveness with higher security.

In the paper, the issue about infrared image enhancement is addressed. First a discrete-type fractional order chaotic system (DFOCS) is constructed. Then a kind of chaotic mapping based on the new system is utilized to design an optimization algorithm for the achievement of infrared image enhancement. In the final, some typical experiments are conducted to display the advantage and effectiveness of the given optimization algorithm, which is much suitable for the potential application in fault detection.

This paper investigates the convergence-enhanced subspace channel estimation technique for multiple-input---multiple-output (MIMO)-orthogonal frequency division multiplexing (OFDM) systems with or without virtual carriers (VCs). Since the perturbations of the correlation matrix and the noise subspace are inversely proportional to the number of OFDM symbols, the subspace channel estimation involves much computational complexity to improve the convergence speed when computing the noise subspace from the correlation matrix of the received signals. Using the block circular property of the channel matrix, the circular repetition method (CRM) is proposed to generate a group of equivalent signals for each OFDM symbol. The subspace estimation of the channel coefficients performs very well within a few OFDM symbols using these equivalent symbols for both the CP-OFDM and ZP-OFDM systems with or without VCs. The computational complexity is analyzed for the CRM-based channel estimation, which reveals that it has an almost identical complexity to the traditional method. Computer simulations demonstrate that the proposed CRM-based blind/semi-blind channel estimation achieves a lower MSE than the other methods.

In this paper, blind and semi-blind subspace channel estimations with fast convergence rate are proposed for multiple-input multiple-output orthogonal frequency division multiplexing (OFDM) systems in the presence of virtual carriers. The subspace method has a main drawback of slow convergence rate when deriving the noise subspaces from the second-order statistics of received signals. This phenomenon is especially evident when the size of received signals is large. Inspired by this fact, we present a block matrix scheme (BMS) to generate a group of sub-vectors from each OFDM symbol when virtual carriers are used. The number of equivalent signals is increased and therefore, the convergence rate of channel estimation is enhanced. The semi-blind method is also investigated by incorporating subspace technology with least square scheme. The identifiability of the BMS-based channel estimation is analyzed to derive the applicable range of the BMS size. The computational complexity of the proposed channel estimation is calculated at the end. Computer simulations show that the proposed blind and semi-blind methods converge very well in channel estimation and equalization.

In this paper, the blind subspace channel estimation using the block matrix scheme is proposed for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. Based on the Toeplitz structure, the block matrix scheme collects a group of the received OFDM symbols into a vector, and then partitions it into a set of equivalent symbols. The number of equivalent symbols is about N times of OFDM symbols, where N is the size of FFT operation. With those equivalent symbols, the proposed blind subspace channel estimation can converge within a small amount of OFDM symbols. The identifiability of the proposed channel estimation is examined that the channel matrix is determined up to an ambiguity matrix. Besides, the semi-blind channel estimation is also investigated by combining few pilot sequences with the subspace method. Simulation results show that the proposed channel estimators perform very well even in a time-varying scenario. Especially the semi-blind methods achieve almost the same BERs as those by true channels.

The repetition index scheme (RIS) is often applied to the channel estimation of orthogonal frequency division multiplexing (OFDM) systems in the scenario that the numbers of receive antennas and transmit antennas are equal. In this paper, we apply RIS method to the channel estimation of cyclic prefixing (CP) multiple-input multiple-output (MIMO) OF DM systems with virtual carriers. The signal model is first simplified to remove the rank deficiency due to the virtual carriers. We use the RIS method and circular matrix property to generate times of equivalent symbols so as that the channel estimation can converge with few received OFDM block. The RIS method suffers from the nonwhite noise effects. We further calculate the noise correlation matrices. Then a pre-whitening technique is developed from the Cholesky factorization of the noise correlation matrix. Computer Simulations wil1 demonstrate the effectiveness of the proposed channel estimation.

A novel noise whitening technique of blind subspace channel estimations is proposed for cyclic prefixing multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. A fast convergence subspace channel estimation for cyclic prefixed MIMO-OFDM systems was presented in [1] where the repetition index was used to increase the number of equivalent received signals. However, the repetition index approach suffers from the nonwhite noise effects. In this paper, the noise correlation matrices are first calculated. Then a prewhitening technique is developed from the Choleskey decomposition of the noise correlation matrix. The noise whitening technique removes the correlation of the non-white noise. Computer Simulations show the effectiveness of the proposed noise whitening method.

The subspace channel estimation technique is investigated for MIMO-OFDM systems with space-time block codes (STBC). The noise subspace computed from the correlation matrix of received signals requires a large number of symbols to converge. Using the block cyclic property of the channel matrix, we propose the cyclic repetition method (CRM) to generate many times of equivalent symbols for each OFDM symbol. With these equivalent symbols, the subspace channel estimation can perform very well within a few OFDM symbols. Computer simulations demonstrate the proposed CRM-based channel estimation has better performance than the conventional ones.

In this paper, the convergence-enhanced subspace channel estimation technique is investigated for MIMO-OFDM systems. The perturbation of noise subspace is inversely proportional to the number of OFDM symbols. Using the block circular property of the channel matrix, we propose the cyclic repetition method (CRM) to generate a group of equivalent signals for each OFDM symbol. The subspace channel estimation can perform very well within a few OFDM symbols using these equivalent symbols for both the CP-OFDM and ZP-OFDM systems. Computer simulations demonstrate the proposed CRM-based channel estimation achieves a lower MSE than the compared ones.