Channel Estimation Method Research Articles

Deep learning for fast channel estimation in millimeter-wave MIMO systems

Channel estimation has been considered as a key issue in the millimeter-wave (mmWave) massive multi-input multioutput (MIMO) communication systems, which becomes more challenging with a large number of antennas. In this paper, we propose a deep learning (DL)-based fast channel estimation method for mmWave massive MIMO systems. The proposed method can directly and effectively estimate channel state information (CSI) from received data without performing pilot signals estimate in advance, which simplifies the estimation process. Specifically, we develop a convolutional neural network (CNN)-based channel estimation network for the case of dimensional mismatch of input and output data, subsequently denoted as channel (H) neural network (HNN). It can quickly estimate the channel information by learning the inherent characteristics of the received data and the relationship between the received data and the channel, while the dimension of the received data is much smaller than the channel matrix. Simulation results show that the proposed HNN can gain better channel estimation accuracy compared with existing schemes.

Semi-Blind Channel Estimation for Intelligent Reflecting Surfaces in Massive MIMO Systems

Intelligent reflecting surface (IRS) is considered as a promising technology for enhancing the transmission rate in cellular networks. Such improvement is attributed to considering a large IRS with high number of passive reflecting elements, optimized to properly focus the incident beams towards the receiver. However, to achieve this beamforming gain, the channel state information (CSI) should be be efficiently acquired at the base station (BS). Unfortunately, the traditional pilot estimation method is challenging, because the passive IRS does not have radio frequency (RF) chains and the number of channel coefficients is proportional to the number of IRS elements. In this paper, we propose a novel semi-blind channel estimation method where the reflected channels are estimated using not only pilot but also data symbols, reducing the channel estimation overhead. The performance of the system is analytically investigated in terms of the uplink achievable sum-rate. The proposed scheme achieves higher energy and spectrum efficiency, while being robust to channel estimation errors. For instance, the proposed scheme achieves a 80% increase in spectrum efficiency compared to pilot-only based schemes, for IRSs with <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</i> = 32 elements.

Low-Complexity Joint CFO and Channel Estimation for RIS-Aided OFDM Systems

Accurate channel estimation is essential for achieving the performance gains offered by reconfigurable intelligent surface (RIS)-aided wireless communications. A variety of channel estimation methods have been proposed for such systems; however, none of the existing methods takes into account the effect of synchronization errors such as carrier frequency offset (CFO). In general, CFO can significantly degrade the channel estimation performance of orthogonal frequency division multiplexing (OFDM) systems. Motivated by this, we investigate the effect of CFO on channel estimation for RIS-aided OFDM systems. Furthermore, we propose a joint CFO and channel impulse response (CIR) estimation method for these systems. Simulation results demonstrate the effectiveness of our proposed method, and also demonstrate that the use of time-domain rather than frequency-domain estimation in this context results in an improvement in the mean-squared error (MSE) performance of channel estimation as well as in a significantly lower overall computational complexity.

Channel Estimation for RIS-Aided Multiuser Millimeter-Wave Systems

Channel estimation in the RIS-aided massive multiuser multiple-input single-output (MU-MISO) wireless communication systems is challenging due to the passive feature of RIS and the large number of reflecting elements that incur high channel estimation overhead. To address this issue, we propose a novel cascaded channel estimation strategy with low pilot overhead by exploiting the sparsity and the correlation of multiuser cascaded channels in millimeter-wave massive MISO systems. Based on the fact that the phsical positions of the BS, the RIS and users may not change in several or even tens of consecutive channel coherence blocks, we first estimate the full channel state information (CSI) including all the angle and gain information in the first coherence block, and then only re-estimate the channel gains in the remaining coherence blocks with much less pilot overhead. In the first coherence block, we propose a two-phase channel estimation method, in which the cascaded channel of one typical user is estimated in Phase I based on the linear correlation among cascaded paths, while the cascaded channels of other users are estimated in Phase II by utilizing the partial CSI of the common base station (BS)-RIS channel obtained in Phase I. The total theoretical minimum pilot overhead in the first coherence block is $8J-2+(K-1)\left\lceil (8J-2)/L\right\rceil $, where $K$, $L$ and $J$ denote the numbers of users, paths in the BS-RIS channel and paths in the RIS-user channel, respectively. In each of the remaining coherence blocks, the minimum pilot overhead is $JK$. Moreover, the training phase shift matrices at the RIS are optimized to improve the estimation performance.

Secure Transmission Scheme Based on Fingerprint Positioning in Cell-Free Massive MIMO Systems

In this paper, we mainly study how to improve the secure transmission performance of cell-free massive multiple-input multiple-output (MIMO) systems by using location technology. In cell-free massive MIMO systems, active pilot attacks will contaminate the uplink channel estimation and affect the downlink precoding of access points (APs). In order to effectively reduce the impact of active pilot attacks on user transmission, this paper respectively proposes an user location estimation method based on fingerprint positioning and a channel estimation algorithm based on location information. Firstly, under the imperfect channel state information, the location information of users and eavesdropper is obtained by using fingerprint positioning method and K-means clustering algorithm. Then, combined with location information, AP selection strategy and channel estimation method based on non-overlapping angle of arrival (AOA) criterion are proposed respectively. Based on the location information of users and eavesdropper, we use discrete Fourier transform (DFT) to distinguish the uplink channels of legitimate user and eavesdropper from the angle domain, thus eliminating the pilot contamination caused by active pilot attacks. The results show that compared with the traditional transmission method, the proposed secure transmission strategy can increase the secrecy rate of up to 2 b/s/Hz, which effectively enhances the secure transmission performance of cell-free massive MIMO in strong interference environment.

LSTM-Based Distributed Conditional Generative Adversarial Network for Data-Driven 5G-Enabled Maritime UAV Communications

5G enabled maritime unmanned aerial vehicle (UAV) communication is one of the important applications of 5G wireless network which requires minimum latency and higher reliability to support mission-critical applications. Therefore, lossless reliable communication with a high data rate is the key requirement in modern wireless communication systems. These all factors highly depend upon channel conditions. In this work, a channel model is proposed for air-to-surface link exploiting millimeter wave (mmWave) for 5G enabled maritime unmanned aerial vehicle (UAV) communication. Firstly, we will present the formulated channel estimation method which directly aims to adopt channel state information (CSI) of mmWave from the channel model inculcated by UAV operating within the Long Short Term Memory (LSTM)-Distributed Conditional generative adversarial network (DCGAN) i.e. (LSTM-DCGAN) for each beamforming direction. Secondly, to enhance the applications for the proposed trained channel model for the spatial domain, we have designed an LSTM-DCGAN based UAV network, where each one will learn mmWave CSI for all the distributions. Lastly, we have categorized the most favorable LSTM-DCGAN training method and emanated certain conditions for our UAV network to increase the channel model learning rate. Simulation results have shown that the proposed LSTM-DCGAN based network is vigorous to the error generated through local training. A detailed comparison has been done with the other available state-of-the-art CGAN network architectures i.e. stand-alone CGAN (without CSI sharing), Simple CGAN (with CSI sharing), multi-discriminator CGAN, federated learning CGAN and DCGAN. Simulation results have shown that the proposed LSTM-DCGAN structure demonstrates higher accuracy during the learning process and attained more data rate for downlink transmission as compared to the previous state of artworks.

ODPM Channel Estimation Method using Multiple MRC and New Reliability Test in IEEE 802.11p Systems with Receive Diversity

ODPM Channel Estimation Method using Multiple MRC and New Reliability Test in IEEE 802.11p Systems with Receive Diversity

Experience-driven learning-based intelligent hybrid beamforming for massive MIMO mmWave communications

We have studied massive MIMO hybrid beamforming (HBF) for millimeter-wave (mmWave) communications, where the transceivers only have a few radio frequency chain (RFC) numbers compared to the number of antenna elements. We propose a hybrid beamforming design to improve the system’s spectral, hardware, and computational efficiencies, where finding the precoding and combining matrices are formulated as optimization problems with practical constraints. The series of analog phase shifters creates a unit modulus constraint, making this problem non-convex and subsequently incurring unaffordable computational complexity. Advanced deep reinforcement learning techniques effectively handle non-convex problems in many domains; therefore, we have transformed this non-convex hybrid beamforming optimization problem using a reinforcement learning framework. These frameworks are solved using advanced deep reinforcement learning techniques implemented with experience replay schemes to maximize the spectral and learning efficiencies in highly uncertain wireless environments. We developed a twin-delayed deep deterministic (TD3) policy gradient-based hybrid beamforming scheme to overcome Q-learning’s substantial overestimation. We assumed a complete channel state information (CSI) to design our beamformers and then challenged this assumption by proposing a deep reinforcement learning-based channel estimation method. We reduced hybrid beamforming complexity using soft target double deep Q-learning to exploit mmWave channel sparsity. This method allowed us to construct the analog precoder by selecting channel dominant paths. We have demonstrated that the proposed approaches improve the system’s spectral and learning efficiencies compared to prior studies. We have also demonstrated that deep reinforcement learning is a versatile technique that can unleash the power of massive MIMO hybrid beamforming in mmWave systems for next-generation wireless communication.

Resource Allocation for Intelligent Reflecting Surfaces Assisted Federated Learning System with Imperfect CSI

Due to its ability to significantly improve the wireless communication efficiency, the intelligent reflective surface (IRS) has aroused widespread research interest. However, it is a challenge to obtain perfect channel state information (CSI) for IRS-related channels due to the lack of the ability to send, receive, and process signals at IRS. Since most of the existing channel estimation methods are developed to obtain cascaded base station (BS)-IRS-user devices (UDs) channel, this paper studies the problem of computation and communication resource allocation of the IRS-assisted federated learning (FL) system based on the imperfect CSI. Specifically, we take the statistical CSI error model into consideration and formulate the training time minimization problem subject to the rate outage probability constraints. In order to solve this issue, the semi-definite relaxation (SDR) and the constrained concave convex procedure (CCCP) are invoked to transform it into a convex problem. Subsequently, a low-complexity algorithm is proposed to minimize the delay of the FL system. Numerical results show that the proposed algorithm effectively reduces the training time of the FL system base on imperfect CSI.

Orthogonal Time Sequency Multiplexing Modulation: Analysis and Low-Complexity Receiver Design

This paper proposes orthogonal time sequency multiplexing (OTSM), a novel single carrier modulation scheme that places information symbols in the delay-sequency domain followed by a cascade of time-division multiplexing (TDM) and Walsh-Hadamard sequence multiplexing. Thanks to the Walsh Hadamard transform (WHT), the modulation and demodulation do not require complex domain multiplications. For the proposed OTSM, we first derive the input-output relation in the delay-sequency domain and present a low complexity detection method taking advantage of zero-padding. We demonstrate via simulations that OTSM offers high performance gains over orthogonal frequency division multiplexing (OFDM) and similar performance to orthogonal time frequency space (OTFS), but at lower complexity owing to WHT. Then we propose a low complexity time-domain channel estimation method. Finally, we show how to include an outer error control code and a turbo decoder to improve error performance of the coded system.

Study of the STBC based MIMO System Modeling and Evaluation

There are many applications using the small MIMO antenna structure for performance improvement. The Long Term Evolution (LTE) and Wi-Fi wireless modems are good example of the MIMO based modeling. Many coding techniques have been designed for enhancing the performance of the MIMO channels. This paper has presented the comprehensive study of the Coding techniques used for MIMO system specially using the Space Time Block Codes (STBC). Basically the coding techniques are used for improving the estimation of the MIMO Channel states information (CSI). Various design challenges of the channel estimation for wireless communication are addressed and problems are identified. The comparison of various SCBC based MIMO channel estimation methods have been tabulated based on the BER and MSE performances.

Wireless ultraviolet scattering channel estimation method based on deep learning.

Due to the strong scattering characteristics, there are serious problems of inter-symbol interference (ISI) and transmission attenuation in the none-line-of-sight (NLOS) wireless ultraviolet communication system. In this paper, a wireless ultraviolet scattering channel estimation method based on deep learning is presented. The learning model structure is designed by combining the one-dimensional convolutional neural network (1D-CNN) and the deep neural network (DNN). In the training stage, the network optimization process is improved by the differential evolution (DE) algorithm. The computer simulation results show that the proposed deep learning channel estimation scheme has better mean square error (MSE) performance and bit error rate (BER) performance compared with the traditional algorithms. Furthermore, we verify the stability of this scheme in different communication environments, and the constructed neural network model has good generalization ability.

Cost-Efficient RIS-Aided Channel Estimation via Rank-One Matrix Factorization

A reconfigurable intelligent surface (RIS) consists of massive meta elements, which can improve the performance of future wireless communication systems. Existing RIS-aided channel estimation methods try to estimate the cascaded channel directly, incurring high computational and training overhead especially when the number of elements of RIS is extremely large. In this letter, we propose a cost-efficient channel estimation method via rank-one matrix factorization (MF). Specifically, if the RIS is employed near base station (BS), it is found that the RIS-aided channel can be factorized into a product of low-dimensional matrices. To estimate these factorized matrices, we propose alternating minimization and gradient descent approaches to obtain the near optimal solutions. Compared to directly estimating the cascaded channel, the proposed MF method reduces training overhead substantially. Finally, the numerical simulations show the effectiveness of the proposed MF method.

Fast Fading Channel Estimation for OFDM Systems with Complexity Reduction

Channel estimation plays a significant role in the Inter-carrier interference (ISI) mitigation and symbol detection in Orthogonal frequency division multiplexing (OFDM) systems. As the speed of mobile terminal increases, the channel changes rapidly, causing fast fading which will degrade the system performance. In this paper, a low complexity fast fading channel estimation method suited for scattered pilot OFDM systems is proposed. This method is characterized by estimating channel matrix directly in the frequency domain, which can significantly reduce the channel estimation complexity with good system performance. Simulation results verify its superiority over the existing estimation methods.

Transmission Schemes and Power Allocation for Multiuser Massive MIMO Relaying

This paper proposes and analyzes the performance of two simple transmission protocols for a multiuser massive multiple input multiple-output relaying system, where K single-antenna users transmit data to a massive-antenna destination through an N-antenna relay node. In the first transmission protocol, the relay does not need to know the CSI. It just amplifies and forwards the received signals to the destination. In the second protocol, the relay first estimates the channels from all users. It then uses the maximum-ratio combining (MRC) technique to combine all received signals and forwards them to the destination. In both protocols, the destination estimates the channels and employs MRC to decode the signals. We propose an efficient channel estimation method at the destination in which the destination estimates only the effective channels gains. As a consequence, the channel estimation overhead does not depend on the numbers of relay and destination antennas. We derive closed-form expressions for the spectral efficiency of the two transmission protocols. These results allow us to further analyze the system performance and to allocate the transmit powers. Particularly, a max-min power control algorithm is proposed which selects the transmit powers at the relay and users to maximize the lowest spectral efficiency of all users. We show that, by using the max-min power allocation algorithm, the spectral efficiency can be increased significantly, compared to uniform power allocation. Furthermore, if the distance between the users and the relay is large, the first transmission protocol is better and vice versa if this distance is small.

Knowledge Distillation-Aided End-to-End Learning for Linear Precoding in Multiuser MIMO Downlink Systems With Finite-Rate Feedback

We propose a deep learning-based channel estimation, quantization, feedback, and precoding method for downlink multiuser multiple-input and multiple-output systems. In the proposed system, channel estimation and quantization for limited feedback are handled by a receiver deep neural network (DNN). Precoder selection is handled by a transmitter DNN. To emulate the traditional channel quantization, a binarization layer is adopted at each receiver DNN, and the binarization layer is also used to enable end-to-end learning. However, this can lead to inaccurate gradients, which can trap the receiver DNNs at a poor local minimum during training. To address this, we consider knowledge distillation, in which the existing DNNs are jointly trained with an auxiliary transmitter DNN. The use of an auxiliary DNN as a teacher network allows the receiver DNNs to additionally exploit lossless gradients, which is useful in avoiding a poor local minimum. For the same number of feedback bits, our DNN-based precoding scheme can achieve a higher downlink rate compared to conventional linear precoding with codebook-based limited feedback.

Wideband Channel Estimation for IRS-Aided Systems in the Face of Beam Squint

Intelligent reflecting surfaces (IRSs) improve both the bandwidth and energy efficiency of wideband communication systems by using low-cost passive elements for reflecting the impinging signals with adjustable phase shifts. To realize the full potential of IRS-aided systems, having accurate channel state information (CSI) is indispensable, but it is challenging to acquire, since these passive devices cannot carry out transmit/receive signal processing. The existing channel estimation methods conceived for wideband IRS-aided communication systems only consider the channel's frequency selectivity, but ignore the effect of beam squint, despite its severe performance degradation. Hence we fill this gap and conceive wideband channel estimation for IRS-aided communication systems by explicitly taking the effect of beam squint into consideration. We demonstrate that the mutual correlation function between the spatial steering vectors and the cascaded two-hop channel reflected by the IRS has two peaks, which leads to a pair of estimated angles for a single propagation path, due to the effect of beam squint. One of these two estimated angles is the frequency-independent `actual angle', while the other one is the frequency-dependent `false angle'. To reduce the influence of false angles on channel estimation, we propose a twin-stage orthogonal matching pursuit (TS-OMP) algorithm.

A Low Complexity Learning-Based Channel Estimation for OFDM Systems With Online Training

In this paper, we devise a highly efficient machine learning-based channel estimation for orthogonal frequency division multiplexing (OFDM) systems, in which the training of the estimator is performed online. A simple learning module is employed for the proposed learning-based estimator. The training process is thus much faster and the required training data is reduced significantly. Besides, a training data construction approach utilizing least square (LS) estimation results is proposed so that the training data can be collected during the data transmission. The feasibility of this novel construction approach is verified by theoretical analysis and simulations. Based on this construction approach, two alternative training data generation schemes are proposed. One scheme transmits additional block pilot symbols to create training data, while the other scheme adopts a decision-directed method and does not require extra pilot overhead. Simulation results show the robustness of the proposed channel estimation method. Furthermore, the proposed method shows better adaptation to practical imperfections compared with the conventional minimum mean-square error (MMSE) channel estimation. It outperforms the existing machine learning-based channel estimation techniques under varying channel conditions.

GAMP-SBL-based channel estimation for millimeter-wave MIMO systems

Based on the finite scattering characters of the millimeter-wave multiple-input multiple-output (MIMO) channel, the mmWave channel estimation problem can be considered as a sparse signal recovery problem. However, most traditional channel estimation methods depend on grid search, which may lead to considerable precision loss. To improve the channel estimation accuracy, we propose a high-precision two-stage millimeter-wave MIMO system channel estimation algorithm. Since the traditional expectation–maximization-based sparse Bayesian learning algorithm can be applied to handle this problem, it spends lots of time to calculate the E-step which needs to compute the inversion of a high-dimensional matrix. To avoid the high computation of matrix inversion, we combine damp generalized approximate message passing with the E-step in SBL. We then improve a refined algorithm to handle the dictionary matrix mismatching problem in sparse representation. Numerical simulations show that the estimation time of the proposed algorithm is greatly reduced compared with the traditional SBL algorithm and better estimation performance is obtained at the same time.

Read Reference Calibration and Tracking for Non-Volatile Flash Memories

The performance and reliability of nonvolatile NAND flash memories deteriorate as the number of program/erase cycles grows. The reliability also suffers from cell-to-cell interference, long data retention time, and read disturb. These processes effect the read threshold voltages. The aging of the cells causes voltage shifts which lead to high bit error rates (BER) with fixed predefined read thresholds. This work proposes two methods that aim on minimizing the BER by adjusting the read thresholds. Both methods utilize the number of errors detected in the codeword of an error correction code. It is demonstrated that the observed number of errors is a good measure for the voltage shifts and is utilized for the initial calibration of the read thresholds. The second approach is a gradual channel estimation method that utilizes the asymmetrical error probabilities for the one-to-zero and zero-to-one errors that are caused by threshold calibration errors. Both methods are investigated utilizing the mutual information between the optimal read voltage and the measured error values. Numerical results obtained from flash measurements show that these methods reduce the BER of NAND flash memories significantly.