Channel Estimation Task Research Articles

Visible Light Integrated Positioning and Communication: A Multi-Task Federated Learning Framework

Recently, visible light positioning and visible light communication are becoming a promising technology for integrated sensing and communication. However, the isolated design of positioning and communication has limited the system efficiency and performance. In this paper, a visible light integrated positioning and communication (VIPAC) framework is formulated, in which the positioning task for the sensing service and the channel estimation task for the communication service are integrated into a unified architecture. Firstly, a multi-task learning architecture, which is composed of a sparsity-aware shared network and two task-oriented sub-networks, is proposed to fully exploit the inherent sparse features of visible light channels, and achieve mutual benefits between the two tasks. The depth of the shared network can be adaptively adjusted to extract the optimal shared features, and the two sub-networks are further optimized for the two tasks, respectively. Moreover, the emerging federated learning technique is introduced to devise a multi-user cooperative VIPAC scheme, which further improves the generalization ability in spatiotemporally nonstationary environments while preserving data privacy. It is shown by theoretical analysis and simulation results that, the proposed scheme can significantly improve the performance of positioning and channel estimation in spatiotemporally nonstationary environments compared with existing benchmark schemes.

Near-Field Channel Estimation for Extremely Large-Scale Array Communications: A Model-Based Deep Learning Approach

Extremely large-scale massive MIMO (XL-MIMO) has been reviewed as a promising technology for future wireless communications. The deployment of XL-MIMO, especially at high-frequency bands, leads to users being located in the near-field region instead of the conventional far-field. This letter proposes efficient model-based deep learning algorithms for estimating the near-field wireless channel of XL-MIMO communications. In particular, we first formulate the XL-MIMO near-field channel estimation task as a compressed sensing problem using the spatial gridding-based sparsifying dictionary and then solve the resulting problem by applying the Learning Iterative Shrinkage and Thresholding Algorithm (LISTA). Due to the near-field characteristic, the spatial gridding-based sparsifying dictionary may result in low channel estimation accuracy and a heavy computational burden. To address this issue, we propose a novel sparsifying dictionary learning-LISTA (SDL-LISTA) algorithm that formulates the sparsifying dictionary as a neural network layer and embeds it into LISTA neural network. The numerical results show that our proposed algorithms outperform non-learning benchmark schemes, and SDL-LISTA achieves better performance than LISTA with ten times atoms reduction.

Conditional GAN Empowered Channel Estimation and Pilot Design for Reconfigurable Intelligent Surfaces assisted Symbiotic Radio System

Reconfigurable intelligent surfaces (RISs) has been proven to be a promising technology to improve the performance of symbiotic radio (SR) systems. To take full advantage of RIS-assisted communications, it is crucial to obtain accurate channel state information for high-gain beamforming. However, the statistical distribution of the cascaded channels in a RIS-assisted SR system is complex, which gives rise to a huge challenge for channel estimation tasks. To address this issue, we propose a joint optimization method of channel estimation for the RIS-assisted SR system with pilot design based on deep learning (JPCGAN) in this paper. The proposed scheme uses a fully connected network for pilot design and develops a conditional generative adversarial network (cGAN) to predict the true channels. cGAN is especially helpful in trainning the network model properly and enhance the system’s robustness with an adaptive loss function. It can be obtained by simulation that compared with current methods, the JPCGAN scheme proposed in this paper can significantly improve the accuracy of channel estimation under low signal-to-noise radios.

Multivariate and Global Particle Swarm Algorithm Optimization in mmWave Massive MIMO for Angle Domain Channel Estimation

Exploitation of massive Multiple-Input Multiple-Output gains for downlink transmission in Millimetre Wave Systems comes at the expense of obtaining accurate channel estimation and computing complexity. Moreover, the fundamental task of massive Multiple-Input Multiple-Output channel estimation is to exploit the characteristics of the channel and sparsity of these multi-antennas systems to simplify complicated spatial structures. We tackle the channel estimation problem in constructing angle domain channel model with multivariate optimization. In the proposed global particle swarm optimization for angle domain aided scheme, the channel estimation design is decoupled into two parts The simulation results are provided to demonstrate the superior performance of the proposed algorithm over the traditional CS-based channel estimation methods.

Cognitive Radios Equipped With Modulation and STBC Recognition Over Coded Transmissions

Signal recognition has recently emerged as a key ingredient for cognitive radios with defense and industrial uses. This letter area encompasses the recognition of a broad range of transmission aspects such as modulation format, space-time mapping, channel coding form, central frequency, and data rate. Each of these aspects has been extensively researched in the literature. Only a few works addressed co-recognition. To the best of the authors’ knowledge, there is a single study that is dedicated to the recognition of combined modulation and space-time block coding (STBC), with the constraints of having more receive antennas than transmit antennas and operating over frequency-flat channels. In this letter, we propose a novel algorithm that recognizes modulation and STBC simultaneously over unknown frequency-selective channels while relaxing the requirement of having more antennas at the receiver than at the transmitter. The mathematical treatments demonstrate how an iterative expectation-maximization strategy is simply used to create a maximum-likelihood solution for this problem. Additionally, we make use of soft information coupled with channel decoders to enhance the proposed algorithm’s recognition performance. The proposed design also incorporates the supplementary task of channel estimation as a part of its overall structure. According to the findings of the computational complexity analysis and simulation results, the proposed algorithm easily defeats the one documented in the literature.

MMV-Based Sequential AoA and AoD Estimation for Millimeter Wave MIMO Channels

The fact that the millimeter-wave (mmWave) multiple-input multiple-output (MIMO) channel has sparse support in the spatial domain has motivated recent compressed sensing (CS)-based mmWave channel estimation methods, where the angles of arrivals (AoAs) and angles of departures (AoDs) are quantized using angle dictionary matrices. However, the existing CS-based methods usually obtain the estimation result through one-stage channel sounding that have two limitations: (i) the requirement of large-dimensional dictionary and (ii) unresolvable quantization error. These two drawbacks are irreconcilable; improvement of the one implies deterioration of the other. To address these challenges, we propose, in this paper, a two-stage method to estimate the AoAs and AoDs of mmWave channels. In the proposed method, the channel estimation task is divided into two stages, Stage I and Stage II. Specifically, in Stage I, the AoAs are estimated by solving a multiple measurement vectors (MMV) problem. In Stage II, based on the estimated AoAs, the receive sounders are designed to estimate AoDs. The dimension of the angle dictionary in each stage can be reduced, which in turn reduces the computational complexity substantially. We then analyze the successful recovery probability (SRP) of the proposed method, revealing the superiority of the proposed framework over the existing one-stage CS-based methods. We further enhance the reconstruction performance by performing resource allocation between the two stages. We also overcome the unresolvable quantization error issue present in the prior techniques by applying the atomic norm minimization method to each stage of the proposed two-stage approach. The simulation results illustrate the substantially improved performance with low complexity of the proposed two-stage method.

An Overview of Massive MIMO for 5G and 6G

Massive MIMO is a disruptive technology that was born more than a decade ago, and that has been adopted as an integral part of the 5G standard. Without its many advantages, the next generation of communications systems would not be able to meet most of the requirements necessary to be called 5G. However, several challenges had to be solved from its inception until its adoption. Nowadays, with most of them overcome, researchers are turning their attention to novel ways to enhance the technology and have it once more being an essential feature of the next generation of communications systems, e.g., 6G. This work provides a brief but comprehensive overview of the technology known as Massive MIMO, its challenges, available solutions, and novel research directions. Additionally, we provide discussions on the use of Massive MIMO in Terahertz bands and the use of Machine Learning algorithms for channel estimation and signal detection tasks in such systems.

Online Downlink Multi-User Channel Estimation for mmWave Systems Using Bayesian Neural Network

We propose a Bayesian deep learning framework for model driven online sparse channel estimation task in Multi-user MIMO systems. Tools from Bayesian neural network and stochastic variational Bayesian Inference are utilized to capture aleatoric and epistemic uncertainty estimates. We treat the network prediction as an auxiliary variable to allow inference performance to be unaffected by the stage of training of the network. In addition to providing uncertainty estimates, being Bayesian, the framework enables us the possibility to marginalize over penalty parameters and is well suited for online scenario with changing environments. Our simulations show that the framework is robust to model mismatch, and efficiently captures uncertainty in the predictions.

Fast Channel Estimation in the Transformed Spatial Domain for Analog Millimeter Wave Systems

Fast channel estimation in millimeter-wave (mmWave) systems is a fundamental enabler of high-gain beamforming, which boosts coverage and capacity. The channel estimation stage typically involves an initial beam training process where a subset of the possible beam directions at the transmitter and receiver is scanned along a predefined codebook. Unfortunately, the high number of transmit and receive antennas deployed in mmWave systems increase the complexity of the beam selection and channel estimation tasks. In this work, we tackle the channel estimation problem in analog systems from a different perspective than used by previous works. In particular, we propose to move the channel estimation problem from the angular domain into the transformed spatial domain, in which estimating the angles of arrivals and departures corresponds to estimating the angular frequencies of paths constituting the mmWave channel. The proposed approach, referred to as transformed spatial domain channel estimation (TSDCE) algorithm, exhibits robustness to additive white Gaussian noise by combining low-rank approximations and sample autocorrelation functions for each path in the transformed spatial domain. Numerical results evaluate the mean square error of the channel estimation and the direction of arrival estimation capability. TSDCE significantly reduces the first, while exhibiting a remarkably low computational complexity compared with well-known benchmarking schemes.

Towards Overfitting Avoidance: Tuning-Free Tensor-Aided Multi-User Channel Estimation for 3D Massive MIMO Communications

Channel estimation has long been deemed as one of the most critical problems in three-dimensional (3D) massive multiple-input multiple-output (MIMO), which is recognized as the leading technology that enables 3D spatial signal processing in the fifth-generation (5G) wireless communications and beyond. Recently, by exploring the angular channel model and tensor decompositions, the accuracy of single-user channel estimation for 3D massive MIMO communications has been significantly improved given a limited number of pilot signals. However, these existing approaches cannot be straightforwardly extended to the multi-user channel estimation task, where the base station (BS) aims at acquiring the channels of multiple users at the same time. The difficulty is that the coupling among multiple users' channels makes the channel estimation deviate from widely-used tensor decompositions. It gives a non-standard tensor decomposition format that has not been well tackled. To overcome this challenge, besides directly fitting the new tensor model for channel estimation to the wireless data via block coordinate descent (BCD) method, which is prone to the overfitting of noises or requires regularization parameter tuning, we further propose a novel tuning-free channel estimation algorithm that can automatically control the channel model complexity and thus effectively avoid the overfitting. Numerical results are presented to demonstrate the excellent performance of the proposed algorithm in terms of both estimation accuracy and overfitting avoidance.

Implementation of Deep Learning Based Method for Optimizing Spatial Diversity MIMO Communication

As an alternative solution of the isuue trade-off phenomenon between performance and computational complexity always become the hugest dilemma suffered by researchers, this research proposes an optimization in spatial diversity MIMO communication system using end-to-end learning based model, specifically, it adapts autoencoder model. Two models are introduced in this research which each of them address a problem about data detection task and channel estimation task that has not been addressed in the previous research. The proposed models were evaluated in one of the most common channel impairment which is Rayleigh fading with additional Additive White Gaussian Noise (AWGN) and compared to the standard Alamouti scheme. The results show that these deep learning based models for MIMO communication system result in very promising results by outperforming the baseline methods. In perfect CSIR (Channel State Information in Receiver side) case, the proposed models achieve BER nearly at SNR 22.5 dB. While in channel estimation case, the proposed models can exceed the baseline performance even by only transmitting 2 pilots.

SVM-Based Channel Estimation and Data Detection for One-Bit Massive MIMO Systems

The use of low-resolution Analog-to-Digital Converters (ADCs) is a practical solution for reducing cost and power consumption for massive Multiple-Input-Multiple-Output (MIMO) systems. However, the severe nonlinearity of low-resolution ADCs causes significant distortions in the received signals and makes the channel estimation and data detection tasks much more challenging. In this paper, we show how \textit{Support Vector Machine} (\textit{SVM}), a well-known supervised-learning technique in machine learning, can be exploited to provide efficient and robust channel estimation and data detection in massive MIMO systems with one-bit ADCs. First, the problem of channel estimation for uncorrelated channels is formulated as a conventional SVM problem. The objective function of this SVM problem is then modified for estimating spatially correlated channels. Next, a two-stage detection algorithm is proposed where SVM is further exploited in the first stage. The performance of the proposed data detection method is very close to that of Maximum-Likelihood (ML) data detection when the channel is perfectly known. We also propose an SVM-based joint Channel Estimation and Data Detection (CE-DD) method, which makes use of both the to-be-decoded data vectors and the pilot data vectors to improve the estimation and detection performance. Finally, an extension of the proposed methods to OFDM systems with frequency-selective fading channels is presented. Simulation results show that the proposed methods are efficient and robust, and also outperform existing ones.

LSRN: A Recurrent Residual Learning Framework for Continuous Wireless Channel Estimation Using Super-Resolution Concept

As only a few parts of wireless resources can be utilized for pilot transmission, channel estimation, especially the interpolation process, has often been recognized as a challenging ill-posed reconstruction problem. To deal with this task, we formulate it as a typical image super resolution problem, and propose a recurrent residual learning framework named LSRN. Our proposed scheme jointly utilizes the advantages of recurrent and residual structure in the machine learning area to approximate the non-linear interpolation relations between the reference signal and surrounding resource elements. In addition, we propose a low complexity implementation scheme called LSRN-L to address the stringent processing delay requirement in the channel estimation tasks. Through numerical examples as well as prototype verification, the proposed LSRN/LSRN-L can easily outperform the convolutional GI plus DFT based interpolation scheme by 10dB in terms of normalized mean square error. Meanwhile, the low complexity LSRN-L can maintain the processing delay within one millisecond.

Generalized Least Squares Based Channel Estimation for FBMC-OQAM

The presence of intrinsic inter-carrier interference (ICI) and inter-symbol interference (ISI) at the output of the matched filter receiver in filter bank multi-carrier with offset quadrature amplitude modulation (FBMC-OQAM) systems complicates the task of channel estimation (CE). The conventional approach is to use a pilot symbol preamble structure, such as in the interference approximation/cancellation methods (IAM/ICM), together with a simple virtual symbol (VS) based CE scheme. However, this virtual symbol approach ignores the residual interference from the data portion of the FMBC burst as well as the correlation of the noise across the sub-carriers; both of these factors ultimately limit the performance. In this paper, we propose a generalized least squares (GLS) based CE scheme which, when applied to the IAM/ICM pilot structures, takes into account both the correlated noise and data interference effects. Closed-form expressions are derived for the channel estimation mean-squared error (CE-MSE) of the proposed GLS method and for the conventional VS method, and it is shown that the proposed method leads to a significant reduction of the CE-MSE by up to 6 dB when compared with the VS based method. This is demonstrated to result in improved bit error rate (BER) performance, especially when higher-order modulation is employed, making it an attractive channel estimation method for many modern deployment scenarios. The proposed GLS-based channel estimation scheme is parameterized allowing a flexible trade-off between channel frequency selectivity, estimation complexity and CE-MSE gain. Finally, symmetry properties of the intrinsic interference coefficients for a general FBMC-OQAM system are also proved, which facilitate the computation of the noise and interference autocorrelation matrices required by the proposed algorithm.

A Message-Passing Receiver for OFDM-Based Self-Interference-Limited Networks

In this paper, we develop a reliable bandwidth-efficient solution for coherent reception in networks prone to self-interference. Our primary focus is on orthogonal-frequency-division-multiplexing (OFDM)-based amplify-and-forward (AF) two-way relaying, where each source node suffers from reradiation of its own signal. An important issue is the superposition of the desired and interfering signals, which makes the channel estimation task difficult, particularly for varying channel coefficients. To address this challenging problem, we introduce a factor graph model of the estimation and detection problem at hand. Exploiting the conditional independence between unknown variables in the factor graph, an efficient message-passing algorithm, which performs joint Bayesian channel estimation, self-interference mitigation, and decoding, is derived. Simulation results show that the performance of the proposed method is close to that given by perfect knowledge of the channel for the target and interfering signals, even with limited training overhead.

Channel estimation for multi‐input multi‐output relay systems using the PARATUCK2 tensor model

In this study, the authors present a novel channel estimation algorithm for three-hop multi-input multi-output (MIMO) relay systems using the PARATUCK2 tensor model. At the destination, the proposed algorithm exploits a unified formulation of the received signal as a PARATUCK2 model, and jointly estimates all of the channel matrices involved in the communication. Compared with existing algorithms, the proposed algorithm only requires the source to transmit the channel training sequences, does not need relays to perform any task of channel estimation, and yields smaller estimation error. Moreover, the proposed algorithm can be extended to multi-hop MIMO relay systems with any number of hops. Numerical examples are shown to demonstrate the effectiveness of the PARATUCK2-based channel estimation algorithm.

Iterative channel estimation method for long‐term evolution downlink transmission

Long-term evolution (LTE) standard employs orthogonal frequency division multiplexing (OFDM) for downlink (DL) data transmission. As pilot subcarriers are non-uniformly and sparsely spaced in the OFDM DL transmission, more complicated channel estimation methods may have to be adopted in the mobile devices to ensure adequate performance. An iterative approach is proposed for the channel estimation task, in which an inverse fast Fourier transform and fast Fourier transform pair is iteratively used to smooth channel estimates provided by decision feedback. The performance of this approach is compared with two two-dimensional interpolation methods and one discrete Fourier transform-based estimation method that have been proposed for the LTE DL transmission. Simulation results show that the performance of the proposed method surpasses all the other methods except at high signal-to-noise ratio, and moreover, not a single estimation method consistently performs better than the proposed method for all the channel models considered. Thus, the proposed method is the best among the considered methods for LTE DL transmission.

Resource-efficient fusion with pre-compensated transmissions for cooperative spectrum sensing.

Recently, a novel fusion scheme for cooperative spectrum sensing was proposed for saving resources in the control channel. Secondary users (SUs) simultaneously report their decisions using binary modulations with the same carrier frequencies. The transmitted symbols add incoherently at the fusion centre (FC), leading to a larger set of symbols in which a subset is associated with the presence of the primary user (PU) signal, and another subset is associated with the absence of such a signal. The decision criterion applied for discriminating these subsets works under the assumption that the channel gains are known at the FC. In this paper, we propose a new simultaneous transmission and decision scheme in which the task of channel estimation is shifted from the FC to the SUs, without the need for feeding-back of the estimates to the FC. The estimates are used at the SUs to pre-compensate for the reporting channel phase rotations and to partially compensate for the channel gains. This partial compensation is the result of signal clipping for peak-to-average power ratio (PAPR) control. We show, analytically and with simulations, that this new scheme can produce large performance improvements, yet reduces the implementation complexity when compared with the original one.

Radial Basis Function Neural Networks for Channel Estimation in MIMO-OFDM Systems

Orthogonal frequency division multiplexing (OFDM) combined with multiple input multiple output (MIMO) antennas is one of the promising schemes for high rate data transmission and capacity improvement. However, in these systems, channel estimation task is critical for coherent detection and demodulation. In this study, we have proposed a channel estimator based on radial basis function neural network trained by gradient descent method for MIMO-OFDM systems. Simulation results show that the proposed estimator performs better than other considered channel estimation techniques.

Application-Oriented Least Squares Experiment Design in Multicarrier Communication Systems

Recently, the application-oriented framework for pilot design in communication systems has been introduced. This framework is mostly appropriate for such a design since the training sequences are selected to optimize a final performance metric of interest and not some of the classical metrics quantifying the distance between the estimated model and the true one, e.g., the mean square error (MSE). In this perspective, the known pilot sequences that are optimal for any communication system and for any estimation task have to be reexamined. In this paper, the problem of training pilot design for the task of channel estimation in cyclic prefixed orthogonal frequency division multiplexing (CP-OFDM) systems is revisited. So far, the optimal training sequences for least squares (LS) channel estimation with respect to minimizing the channel MSE under a training energy constraint have been derived. Here, we investigate the same problem for the LS channel estimator, but when the design takes into account an end performance metric of interest, namely, the symbol estimate MSE due to the use of per subcarrier zero forcing (ZF) symbol estimators/equalizers at the receiver side. Based on some convex approximations, we verify that the optimal full preamble, i.e, the preamble employing pilots on all sub carriers, for LS channel estimation in its classical context are near optimal in the aforementioned application-oriented context for the ZF symbol estimate MSE in certain target signal-to-noise ratio (SNR) operating intervals.