Conventional Channel Estimation Methods Research Articles

Doppler spread analysis for suppressing channel time variation in high‐mobility massive MIMO V2V communications

AbstractHere, fast time‐varying channels of high‐mobility vehicle‐to‐vehicle communications for massive multiple‐input multiple‐output orthogonal frequency division multiplexing systems are considered. Large‐scale uniform linear arrays are configured at the transmitter and receiver to separate multiple angle domain Doppler frequency offsets based on transmit and receive beamforming with high spatial resolution. Then, each beamforming branch comprises only one dominant Doppler frequency offset. Next, the conventional channel estimation method is performed for each beamforming branch, and carry out maximum‐ratio‐combining for data detection. Power spectrum density and Doppler spread of the equivalent link between the transmitter and receiver are derived and regarded as the criterion for assessing the residual channel time variation caused by limited antennas in practice. Interestingly, a scaling law between the asymptotic Doppler spread and the number of transceiver antennas shows that asymptotic Doppler spread is proportional to the maximum Doppler frequency offset and decreases at the rate of , where and are the number of transmit and receive antennas, respectively. Simulation results confirm the validity of the proposed Doppler suppression framework in high‐mobility vehicle‐to‐vehicle communications.

Channel Estimation and Pilot Design for Uplink Sparse Code Multiple Access System based on Complex-Valued Sparse Autoencoder

Channel estimation is one of the most important aspects of wireless communication. Especially in Sparse Code Multiple Access (SCMA) system, the accuracy of channel estimation has a significant impact on decoding performance. Various methods, so far, have been developed for channel estimation. Most of these methods regard channel estimation as a parameter estimation problem of linear models. However, these methods require lots of time-frequency resources to ensure high estimation accuracy. In massive connection scenarios, high pilot overhead makes the spectrum resource more scarce. Therefore, the drawback of conventional channel estimation methods limits the further improvement of system capacity in the Internet of Things(IoT) when time-frequency resources is restricted. To address this problem, in this paper, we propose an efficient channel estimation scheme and sparse pilot structure design method in SCMA system based on complex-valued sparse autoencoder which is effective to learn features of wireless channel. Complex-valued sparse autoencoder is a kind of neural network with complex-valued weights. It contains two parts: encoder and decoder. In our work, the encoder part is used to realize pilot design. Channel estimation is implemented by the decoder. Complex-valued weights obtained from training are used as baseband pilots. Compared with maximum likelihood channel estimation (MLE) of linear model, the proposed method can achieve higher channel estimation accuracy with more sparse pilot structure. The bit-error rates performance of the SCMA receiver in our work is very close to that of the perfect channel state information (CSI).

Deep Learning for Channel Estimation in Physical Layer Wireless Communications: Fundamental, Methods, and Challenges

With the rapid development of wireless communication technology, intelligent communication has become one of the mainstream research directions after the fifth generation (5G). In particular, deep learning has emerged as a significant artificial intelligence technology widely applied in the physical layer of wireless communication for achieving intelligent receiving processing. Channel estimation, a crucial component of physical layer communication, is essential for further information recovery. As a motivation, this paper aims to review the relevant research on applying deep learning methods in channel estimation. Firstly, this paper briefly introduces the conventional channel estimation methods and then analyzes their respective merits and drawbacks. Subsequently, this paper introduces several common types of neural networks and describes the application of deep learning in channel estimation according to data-driven and model-driven approaches, respectively. Then, this paper extends to emerging communication scenarios and discusses the existing research on channel estimation based on deep learning for reconfigurable intelligent surface (RIS)-aided communication systems. Finally, to meet the demands of next-generation wireless communication, challenges and future research trends in deep-learning-based channel estimation are discussed.

Modified entropy based least square channel estimation technique for 5G massive multiple input multiple output universal filtered multicarrier systems

These days’ Massive multiple input multiple output (mMIMO) systems have become popular because of their enhanced data transmission rates, robustness against multipath fading, enhanced spectral efficacy, and ability to communicate with a more significant number of users with immense coverage. The critical challenge of the mMIMO systems is precisely replenishing the channel state information (CSI), along with the synchronization between receiver and transmitter. The CSI has recovered with the help of various channel estimation (CE) techniques. This paper presents a modified entropy-based least square CE technique for 5G mMIMO-UFMC systems. The proposed CE technique performance is novel and better than the Least Square (LS) and minimum mean square error (MMSE) CE techniques. The proposed CE technique was evaluated using MATLAB, and its performance results are shown in the simulation. The performance results of the proposed CE are evaluated based on the mean square error (MSE) and bit error rate (BER) of the obtained signal. The executed results prove that the proposed CE is efficient compared to conventional CE methods. The performance of the MCM techniques with respect to the proposed CE is also presented in this paper. This paper also explains the analytical assessment of the LS, MMSE, and MELS CE techniques. The results show that at high values of SNR, the proposed algorithm outperforms the LS and MMSE for both BER and MSE.

A Joint Channel Estimation and Compression Method Based on GAN in 6G Communication Systems

Due to the increasing popularity of communication devices and vehicles, the channel environment becomes more and more complex, which makes conventional channel estimation methods further increase the pilot overhead to maintain estimation performance. However, it declines the throughput of communication networks. In this paper, we provide a novel two-stage based channel estimation method by using generative adversarial networks (GANs) to handle this problem in orthogonal frequency division multiplexing (OFDM) systems. Specifically, the first stage aims to learn the mapping from a low-dimensional latent variable to the real channel sample. During the second stage, an iterative algorithm method is designed to find the optimal latent variable by matching the pilot channels of a real channel and generated channel. Then, the data channels are recovered based on the learned mapping relationship between the latent variable and the real channel sample. The simulation results show that our proposed method can achieve a performance gain of more than 2 dB with a pilot reduction by 75% when SNR is 10 dB, by comparing with the widely used Wiener filter interpolation method. In addition, as the low-dimensional latent variable can be obtained simultaneously, it can also be used for reducing the feedback overhead.

Deep Learning Based Channel Estimation for MIMO-OFDM System with Modified ResNet Model

Objectives: The effectiveness of wireless communication systems is significantly influenced by channel estimation. In order to accurately estimate the Channel Impulse Response (CIR) of the channel under varied circumstances, channel estimation is a crucial procedure in the functioning of MIMO-OFDM (Multi-Input and Multi-Output – Orthogonal Frequency Division Multiplexing) systems. Methods: The proposed Deep Learning(DL) based Convolutional Neural Network (CNN) with modified ResNet architecture channel estimation method improves the Bit Error Rate(BER) and Mean Square Error(MSE) performance compared to conventional channel estimation methods. We have compared the proposed CNN method with the Least square (LS), Minimum Mean Square Error(MMSE) and Deep Neural Network(DNN) based channel estimation methods. The results have been discussed by using BER and MSE versus SNR graphs. The simulation results are being performed on the MATLAB platform of the R2021b version. Findings: The DL-based MIMO-OFDM channel estimation can achieve better performance over multipath fading channels if Channel coefficients are perfectly estimated at the receiver. The simulation test is carried out in different test conditions by considering the different number of transmitter and receiver antennas with respect to different QAM modulation order values. Novelty: The DL-based modified ResNet architecture comprises a set of layers modified to estimate the optimal channel parameters. For achieving a great reduction of MSE and BER compared to conventional channel estimation methods, the layers of the ResNet– model are modified. Keywords: Channel estimation; MIMOOFDM system; Deep learning; Neural network; ResNet

Channel Estimation for mmWave Massive MIMO Systems based on Deep Learning

Channel estimation is a key part of communication systems. However, the channel of millimeter-Wave (mmWave) Massive Multiple-Input Multiple-Output (Massive-MIMO) system has sparse characteristics, and the conventional channel estimation method is prone to noise factors and tends to achieve low estimation accuracy. Therefore, in this paper a channel estimation method is proposed for mmWave Massive MIMO systems based on deep learning. Firstly, a dataset to simulate a real-world environment, is generated by setting specific parameters. Furthermore, the generated channel matrix is adopted as the input of the neural network. Secondly, the attention mechanism is integrated into the deep learning method with ResUNet to enhance the ability of feature extraction. Finally, the channel estimation matrix is obtained via the aforementioned network model. The experimental results indicate that the Massive-MIMO method is superior to the conventional channel estimation methods in channel estimation accuracy and convergence rate, and has a good application prospects.

Group Successive Interference Cancellation Assisted Semi-Blind Channel Estimation in Multi-Cell Massive MIMO-NOMA Systems

Both non-orthogonal multiple access (NOMA) and massive multiple-input multiple-output (MIMO) are the promising techniques to satisfy the foreseeable massive connectivity requirements. In this letter, a novel semi-blind channel estimation method is proposed for multi-cell massive MIMO-NOMA systems, in which a group successive interference cancellation (GSIC) assisted method is developed to mitigate pilot contamination. Specifically, the users in each cell are divided into multiple groups and the same pilot sets are reused by the different groups in order to reduce pilot overhead. Besides, by following the NOMA principle, a GSIC assisted method is proposed to eliminate inter-group pilot contamination. In simulations, it is shown that the proposed channel estimation method is capable of reducing the overlapping probability of different groups as the number of receiving antennas increases. In addition, the proposed scheme outperforms the conventional channel estimation methods in terms of normalized mean square error (NMSE) performance.

Low complexity channel estimation algorithm using paired spatial signatures for UAV 3D MIMO systems

This paper proposes a low complexity channel estimation algorithm for unmanned aerial vehicle three dimension multi-user multiple-input-multiple-output (3D MIMO) systems with the uniform planar array (UPA) at base station using paired spatial signatures. With the aid of antenna array theory and array signal processing, 3D channel is firstly modeled based on the angles between the direction of arrival along x- and y-axis of the UPA. And 3D MIMO channels can be projected onto the x- and y-directions, respectively. Then, channel estimation for multi-user uplinks using small amount of training resources is divided into two phases. At the first uplink preamble phase, each user is assigned the orthogonal pilot, and the paired spatial signatures and optimal rotation angle of each user through the same pilot sequence are obtained. We also propose a user grouping strategy based on three-dimension angle-division multiple access (3D-ADMA) to ensure that the user's spatial signatures do not overlap. At the second phase during several coherence times, the same pilot sequence within a group and orthogonal pilot sequences between groups are assigned, then, the channel state information of the user's x- and y-directions are recovered by the paired space signatures and optimal rotation angle of each user obtained in the preamble phase, respectively. And dynamically updating the user's paired spatial signatures and optimal rotation angle utilizes the obtained channel parameter of x- and y-directions. Finally, the channel parameter of the x- and y-directions are reconstructed by the updated user's space signatures and the optimal rotation angle, and the 3D MIMO channel estimation is obtained through the Kronecker product. Compared with the conventional channel estimation method of the 3D MIMO system under UPA using a low-rank model, the proposed methods reduce the computational complexity without degrading the estimated performance to a large extent. Furthermore, it is carried out with limited training resources, and the pilot resource overhead of the system is greatly reduced by the 3D-ADMA packet and the two-stage pilot allocation. Simulation results verified that the proposed algorithm is effective and feasible.

Pairs of Pilots Design for Preamble-Based Channel Estimation in OQAM/FBMC Systems

In this letter, we propose an improved pairs of pilots (POP) design method for channel estimation in offset quadrature amplitude modulation based filter bank multicarrier (OQAM/FBMC) systems. Based on an error analysis of the conventional POP channel estimation method, we first develop a heuristic pilot design in which each pilot pair is inserted at two consecutive subcarriers. Besides, we further propose to build a preamble by quartet of subcarriers and conduct a pilot optimization subject to a pilot power constraint, aiming to suppress the noise effect as much as possible. To obtain its optimal solution, we convert the original non-convex pilot optimization problem into a convex semidefinite programming problem, which is shown to be computationally efficient and readily solvable. Numerical simulations validate the effectiveness and superiority of the proposed pilot design method compared to the conventional POP and interference approximation methods.

Projection wavelet weighted twin support vector regression for OFDM system channel estimation

In this paper, an efficient projection wavelet weighted twin support vector regression (PWWTSVR) based orthogonal frequency division multiplexing system (OFDM) system channel estimation algorithm is proposed. Most Channel estimation algorithms for OFDM systems are based on the linear assumption of channel model. In the proposed algorithm, the OFDM system channel is consumed to be nonlinear and fading in both time and frequency domains. The PWWTSVR utilizes pilot signals to estimate response of nonlinear wireless channel, which is the main work area of SVR. Projection axis in optimal objective function of PWWRSVR is sought to minimize the variance of the projected points due to the utilization of a priori information of training data. Different from traditional support vector regression algorithm, training samples in different positions in the proposed PWWTSVR model are given different penalty weights determined by the wavelet transform. The weights are applied to both the quadratic empirical risk term and the first-degree empirical risk term to reduce the influence of outliers. The final regressor can avoid the overfitting problem to a certain extent and yield great generalization ability for channel estimation. The results of numerical experiments show that the propose algorithm has better performance compared to the conventional pilot-aided channel estimation methods.

Kalman Filter Channel Estimation in 2 × 2 and 4 × 4 STBC MIMO-OFDM Systems

Channel estimation is a challenging problem for space time block coding (STBC) multiple-input and multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems in dynamic environments. To handle this problem and improve the system performance, this paper proposes a Kalman filter (KF) based channel estimation method applied to $2\times2$ and $4\times4$ STBC MIMO-OFDM systems. The proposed method based on the dynamic tracking property of KF is well adopted for dynamic channel estimation. First, a new orthogonal space-time codeword is adopted, and the orthogonal pilot sequences are designed to suppress the interference among transmit antennas. Then, the prediction and update characteristics of KF are researched, and the state space model is established for STBC MIMO-OFDM system. Subsequently, the channel state information (CSI) is estimated iteratively according to the KF estimation equation. At last, to further improve the accuracy of KF channel estimation, the threshold is utilized to suppress the noise in the channel impulse response (CIR) estimated by KF method. Simulation results verify that the proposed KF channel estimation method with orthogonal pilots and STBC provides better bit error rate (BER) and normalized mean square error (NMSE) performance compared with other conventional channel estimation methods, and it can be effectively adapted to dynamic multipath propagation conditions with different low and high order modulations.

An Adaptive Channel Estimation Based on Fixed-Point Generalized Maximum Correntropy Criterion

Many conventional adaptive channel estimation methods are based on minimum mean square error (MMSE) criterion, maximum correntropy criterion (MCC) or least p-norm criterion. However, these criteria are not always desirable in the presence of the various kinds of noises in different wireless channels. To further enhance the adaptability of the channel estimation to different noises, this paper introduces a new criterion named generalized maximum correntropy criterion (GMCC), and calculates the estimated channel parameters using the fixed-point iteration. The convergence analysis of the fixed-point iteration based on GMCC is discussed theoretically and is verified by experiments. More experiments are implemented to show the steady-state performance of the algorithm under different noise environments and the guide of parameter selection is provided.

A Novel Noise Suppression Channel Estimation Method Based on Adaptive Weighted Averaging for OFDM Systems

Orthogonal frequency division multiplexing (OFDM) systems have inherent symmetric properties, such as coding and decoding, constellation mapping and demapping, inverse fast Fourier transform (IFFT) and fast Fourier transform (FFT) operations corresponding to multi-carrier modulation and demodulation, and channel estimation is a necessary module to resist channel fading in the OFDM system. However, the noise in the channel will significantly affect the accuracy of channel estimation, which further affects the recovery quality of the final received signals. Therefore, this paper proposes an efficient noise suppression channel estimation method for OFDM systems based on adaptive weighted averaging. The basic idea of the proposed method is averaging the last few channel coefficients obtained from coarse estimation to suppress the noise effect, while the average frame number is adaptively adjusted by combining Doppler spread and signal-to-noise ratio (SNR) information. Meanwhile, to better combat the negative effect brought by Doppler spread and inter-carrier interference (ICI), the proposed method introduces a weighting factor to correct the weighted value of each frame in the averaging process. Simulation results show that the proposed channel estimation method is effective and provides better performance compared with other conventional channel estimation methods.

Soft Decision Control Iterative Channel Estimation for the Internet of Things in 5G Networks

In the fifth generation mobile networks, generalized frequency division multiplexing (GFDM) is expected as the candidate waveform which can flexibly meet the requirements of diverse applications and scenarios for the Internet of Things (IoT) because of its advantages over orthogonal frequency division multiplexing (OFDM). In order to achieve the reliable data transmission in GFDM-based IoT systems, channel estimation (CE) is a prerequisite. However, the 2-D block modulation and the nonorthogonality between subcarriers for GFDM make it almost impossible that the conventional CE methods suitable for OFDM are directly applied to GFDM. To cope with this problem, a soft decision control strategy-based iterative CE (SDC-ICE) method is proposed in this paper. First, the received signal is equalized by the channel frequency response (CFR) from the pilot-based CE. After GFDM demodulation and Turbo decoding, the feedback log-likelihood ratio is utilized to rebuild symbols for data-aided CE by a redesigned Turbo receiver. Subsequently, the feedback information of both current and former iterations is used to improve the reliability of rebuilt symbols. The CFR obtained from SDC-ICE is used for equalization in the next iteration. The performance of SDC-ICE can be improved by increasing the iterations. Finally, the bit error rate (BER) and mean square error (MSE) performances of SDC-ICE and hard decision control strategy-based iterative CE (HDC-ICE) are simulated and evaluated. Simulation results demonstrate that the proposed method has better BER and MSE performance than HDC-ICE within fewer iterations.

DFT‐based channel estimation refinement by clustering in FBMC‐OQAM system

The conventional DFT-based channel estimation method can improve the performance of the least square (LS) or minimum mean square error (MMSE) channel estimation by removing the noise outside the maximum channel delay while the noise inside channel delay still remain. Here, a novel DFT-based pilot-aided channel estimation method for filter bank multicarrier with offset quadrature amplitude modulation (FBMC-OQAM) is proposed. Using clustering and discriminant analysis, this method can separate automatically the noise inside channel delay from channel impulse response (CIR) coefficient of the conventional DFT-based channel estimation. Theoretical analysis and simulation results show that the accuracy of channel estimation can be improved remarkably. Compared with conventional DFT-based channel estimation method and DFT-based channel estimation method with threshold, the proposed DFT-based channel estimation method with clustering has a better bit error ratio (BER) performance in additive white Gaussian noise (AWGN) channel and flat fading channel.

Off-Grid Sparse Bayesian Learning-Based Channel Estimation for MmWave Massive MIMO Uplink

In this letter, an angle domain off-grid channel estimation algorithm for the uplink millimeter wave (mmWave) massive multiple-input and multiple-output systems is proposed. By exploiting spatial sparse structure in mmWave channels, the proposed method is capable of identifying the angles and gains of the scatterer paths. Comparing the conventional channel estimation methods for mmWave systems, the proposed method achieves better performance in terms of mean square error. Numerical simulation results are provided to verify the superiority of the proposed algorithm.

Deep Neural Networks for Channel Estimation in Underwater Acoustic OFDM Systems

Orthogonal frequency division multiplexing (OFDM) provides a promising modulation technique for underwater acoustic (UWA) communication systems. It is indispensable to obtain channel state information for channel estimation to handle the various channel distortions and interferences. However, the conventional channel estimation methods such as least square (LS), minimum mean square error (MMSE) and back propagation neural network (BPNN) cannot be directly applied to UWA-OFDM systems, since complicated multipath channels may cause a serious decline in performance estimation. To address the issue, two types of channel estimators based on deep neural networks (DNNs) are proposed with a novel training strategy in this paper. The proposed DNN models are trained with the received pilot symbols and the correct channel impulse responses in the training process, and then the estimated channel impulse responses are offered by the proposed DNN models in the working process. The experimental results demonstrate that the proposed methods outperform LS, BPNN algorithms and are comparable to the MMSE algorithm in respect to bit error rate and normalized mean square error. Meanwhile, there is no requirement of prior statistics information about channel autocorrelation matrix and noise variance for our proposals to estimate channels in UWA-OFDM systems, which is superior to the MMSE algorithm. Our proposed DNN models achieve better performance using 16QAM than 32QAM, 64QAM, furthermore, the specified DNN architectures help improve real-time performance by saving runtime and storage resources for online UWA communications.

Compressed channel estimation methods for high mobility doubly selective channels in orthogonal frequency division multiplexing systems

In this study, four channel-estimation (CE) methods are proposed for sparse high mobility doubly selective channels. These methods are designed to take advantage of the sparsity of the channel to decrease the training sequence size and the computational complexity of the CE. Two of the new methods use time domain training sequences. In the first method, the pilot is designed such that the time domain channel can be estimated tap-by-tap using an autoregressive procedure. In the second method, a truncated Taylor expansion of the channel phase is applied to linearise the equations for CE and a pseudorandom training sequence is utilised to solve those equations. The last two proposed channel estimators employ frequency domain pilots and are built upon a previously proposed estimator that exploits both the delay and Doppler shift sparsity of the channel. The computational complexity and the spectral efficiency of the methods are compared. In addition, simulations are performed to compare the performance of these estimators in a sparse channel. Simulation results indicate up to 30 dB improvement in the bit error rate calculation for our proposed CE methods compared to the conventional CE methods.

Channel Estimation Based on Statistical Frames and Confidence Level in OFDM Systems

Channel estimation is an important module for improving the performance of the orthogonal frequency division multiplexing (OFDM) system. The pilot-based least square (LS) algorithm can improve the channel estimation accuracy and the symbol error rate (SER) performance of the communication system. In pilot-based channel estimation, a certain number of pilots are inserted at fixed intervals between OFDM symbols to estimate the initial channel information, and channel estimation results can be obtained by one-dimensional linear interpolation. The minimum mean square error (MMSE) and linear minimum mean square error (LMMSE) algorithms involve the inverse operation of the channel matrix. If the number of subcarriers increases, the dimension of the matrix becomes large. Therefore, the inverse operation is more complex. To overcome the disadvantages of the conventional channel estimation methods, this paper proposes a novel OFDM channel estimation method based on statistical frames and the confidence level. The noise variance in the estimated channel impulse response (CIR) can be largely reduced under statistical frames and the confidence level; therefore, it reduces the computational complexity and improves the accuracy of channel estimation. Simulation results verify the effectiveness of the proposed channel estimation method based on the confidence level in time-varying dynamic wireless channels.