Channel Estimation Method Research Articles

A Practical Channel Estimation Strategy for XL-MIMO Communication Systems

Extremely large-scale massive MIMO (XL-MIMO) has been emerged as an innovative technique for future 6G communication. Channel state information (CSI) acquisition is a crucial but challenging task in XL-MIMO wireless systems due to the larger number of base station (BS) antennas. In this letter, we investigate a practical strategy to realize efficient channel estimation for XL-MIMO communication systems with the consideration of hybrid-field channel model. Different from the existing work requiring the specific proportion of the number of far-field and near-field channel paths as a prior knowledge, which is not feasible in practical communications, our method aims to provide a criterion to determine the value of proportion first and then acquire the corresponding estimated channel components. Simulation results show the effectiveness and superiority of our proposed channel estimation method as compared to the benchmarks.

Demodulation Reference Signal (DM-RS) based channel estimation for non-terrestrial networks to support high mobility

This paper provides an adaptive 5G-NR based solution for non-terrestrial networks (NTN) to support the high mobility of the user equipment (UE). A front-loaded demodulation reference signal (DM-RS) symbol pattern with additional orthogonal symbols is proposed to support UE mobility 500 km/h. We develop a 5G-NR link-level simulator (LLS) to analyze the performance of physical downlink shared channel (PDSCH) under the proposed front-loaded DM-RS symbol pattern, tapped delay line (TDL) channel model, and precoding-based beamforming/MIMO schemes. The simulation results show that the proposed channel estimation method based on front-loaded DM-RS helps to improve the link reliability and achieves the maximum spectral efficiency up to 5.05 bps/Hz under 64-QAM, which can also contribute to the evolution of NTN in 6G networks to support the transonic speed mobility of UE.

A multi-fusion integrated end-to-end deep kernel CNN based channel estimation for hybrid range UM-MIMO 6G communication systems

In today’s world, Terahertz Communication (THzCom) is envisioned as a highly promising technology to characterize 6G wireless communication within the range from 0.1 to 10 THz. This will configure emerging wireless applications due to the demand for their unique potential to support terabit-per-second transmission and effectively support ultra-high quality of service requirements. Aiming at massive dual beamforming problem in THzCom, the Ultra-Massive-Multiple Input and Multiple Output (UM-MIMO) with less array based hybrid channel modelling is adopted as the promising solutions. Further, in the case of an ultra-massive number of antennas, the beamforming matrix element is quite high-dimensional. This enlarged dimension of channel contributes to the high complexity of Channel Estimation (CE). To focus on these challenges, we presented a Hybrid Short and Long range Channel Model (HSLCM) based Multi-Fusion based Deep Kernel Convolutional Neural Network (MF-DKCNN) for THz UM-MIMO CE method. HSLCM channel modelling is used to determine the channel parameters of THz propagation paths, such as path loss, azimuth and elevation angles, distance, and the steering vector of the lens array. The MF integrated with the DKCNN model is used to estimate the noise-free received signal from accurate channel parameters and handles high-dimensional matrices with low complexity. MF employs the Channel Intersection Fusion Rule (CI-FR) to combine the received signal and channel matrix for a consistent fusion estimate of the known channel parameters from the system model. The DKCNN network structure has fifteen layers in which the MF estimates the noise-free channel matrices to be processed as training labels. Then, the kernel evaluation is used between layers to solve high dimensions and feature complexity. The proposed model is analysed in an outdoor THz channel scenario, which was simulated on the MATLAB (R2018A) platform. Hence, the evaluation results demonstrate that the proposed method outperforms high CE results with low complexity than other methods in terms of training data and Normalized Mean Square Error (NMSE).

Robust Channel Estimation Scheme for Multi-UAV MmWave MIMO Communication with Jittering

In unmanned aerial vehicle (UAV)-assisted millimeter-wave (mmWave) communications, the communication performance is significantly degraded by UAV jitter. We formulate a UAV-assisted mmWave channel model with hybrid beamforming for the impacts of UAV jitter. Then, we derive the distribution of angle of arrivals/departures (AOAs/AODs) with random fluctuation of the UAV attitude angle. We develop an iterative reweight-based robust scheme as the super-resolution AOAs/AODs estimation method. Specifically, we introduce the partially adaptive momentum (Padam) estimation method to optimize the objective function of the jittering UAV mmWave massive multi-input multioutput (MIMO) system. Finally, compared with existing channel estimation schemes, the proposed UAV mmWave channel estimation method can achieve robust super-resolution performance in AOAs/AODs and path gains estimation with numerical results. Therefore, the proposed channel estimation scheme is very suitable for UAV mmWave massive MIMO communications with jittering.

Deep learning-based energy harvesting with intelligent deployment of RIS-assisted UAV-CFmMIMOs

The ever-evolving internet of things (IoT) has spawned hundreds of wireless sensors that communicate via the internet infrastructure. The lifetime and self-sustainability of these sensors are pivotal factors dictating the performance of respective application infrastructure. Radio frequency energy harvesting (RFEH) technology has exhibited the capability of effectively augmenting the battery lifetime of these sensors. In this work, we introduce a novel framework called CURe, which combines the advantages of cell-free massive multiple-input multiple-output (CFmMIMO) and reconfigurable intelligent surfaces (RISs) to provide uninterrupted energy harvesting for IoT devices through RFEH. CFmMIMO integrates the advantages of distributed systems and massive MIMO, while RIS improves the signal strength of the information transfer and RFEH via its passive reflection capabilities. Moreover, we consider unmanned aerial vehicles (UAVs) equipped with CFmMIMO as mobile access points (APs) to better serve the moving sensory devices. To further enhance RFEH, we propose DeNCE, a channel estimation technique based on deep learning (DL) that eliminates the need for traditional closed-form equation-based channel estimation methods. Through evaluation, we first validate the performance of CURe by comparing it with the modified bisection search for max–min fairness (MBS-MMF) algorithm and later corroborate that DeNCE significantly improves the performances of both models. Finally, to optimize the UAV deployment and ensure continuous RFEH coverage, we propose dARL, a deep reinforcement learning (DRL)-based scheduling framework that enables UAV-CFmMIMO swarms to perform continuous energy harvesting in the coverage area collaboratively.

Channel Estimation for High-Speed Railway Wireless Communications: A Generative Adversarial Network Approach

In high-speed railways, the wireless channel and network topology change rapidly due to the high-speed movement of trains and the constant change of the location of communication equipment. The topology is affected by channel noise, making accurate channel estimation more difficult. Therefore, the way to obtain accurate channel state information (CSI) is the greatest challenge. In this paper, a two-stage channel-estimation method based on generative adversarial networks (cGAN) is proposed for MIMO-OFDM systems in high-mobility scenarios. The complex channel matrix is treated as an image, and the cGAN is trained against it to generate a more realistic channel image. In addition, the noise2noise (N2N) algorithm is used to denoise the pilot signal received by the base station to improve the estimation quality. Simulation experiments have shown the proposed N2N-cGAN algorithm has better robustness. In particular, the N2N-cGAN algorithm can be adapted to the case of fewer pilot sequences.

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.

Integrated Sensing and Communications for UAV Communications With Jittering Effect

Integrated sensing and communications (ISAC) is a key technique for UAV communications. In this letter, we consider the ISAC design for UAV aided communications. Firstly, we build the spatial model under the jittering effects. Meanwhile, we analyze the effect of the UAV movement and attitude variation to the channel. Then, we propose an efficient channel estimation method by utilizing the ISAC technique, where the UAV sensing, the communications, and the control are jointly considered to benefit the system performance. Finally, we provide various simulation results to verify the performance of the proposed method.

Sparse Channel Estimation With Distorted Signals for UWB OFDM Systems

The power amplifier (PA) is an indispensable part of ultra-wideband (UWB) orthogonal frequency division multiplexing (OFDM) systems. Nevertheless, it will produce nonlinear distortions, degrading the accuracy of channel estimation and thus the performance of the system. To improve the system performance, it is essential to investigate channel estimation using distorted signals. Taking advantage of the sparsity of the UWB channel, a sparse channel estimation method using distorted signals is proposed in this letter, referred to as sparse channel estimation combined with power amplifier nonlinear cancellation (SCE-PANC). Within SCE-PANC, the measurement matrix is constructed only with pilot subcarriers and nonlinear distortions will be eliminated iteratively. The numerical results show that the proposed method has a better performance than the state-of-the-art methods.

IRS-Assisted OTFS System: Design and Analysis

Intelligent reflecting surface (IRS) is a recent technology garnering lots of attention amongst the wireless communication research community due to its ability to improve the channel conditions extravagantly. The main feature of IRS technology is its capability to tune the phases of the reflecting elements to trigger a cohesive amalgamation of various multipath. IRS can easily co-exist with almost all technologies, thereby offering flexibility to the designers to come up with attractive systems. Further, orthogonal time frequency space (OTFS) is a recent 2D modulation technique that can mitigate the drawbacks of the traditional orthogonal frequency division multiplexing (OFDM) for fifth generation (5G) and beyond networks, especially in high Doppler wireless environments like vehicle-to-everything (V2X) and millimeter wave (mmWave) communications. This paper presents the system architecture of the IRS-assisted OTFS scheme in the delay-Doppler (DD) domain, including the modulation, demodulation, and detection methods. The selection of the reflection coefficients play the most vital role in the IRS-OTFS system, like any IRS system. IRS reflection optimization cannot perfectly line up all multipath with the same phase in the frequency-selective channels. We adopt an IRS phase optimization method where only the strongest delay-Doppler path is phase-aligned, while the paths with lesser strengths will be out of phase with the direct path. This reconfigurability of IRS elements enhances the OTFS modulation scheme, thereby significantly mitigating the multipath fading effects in high Doppler channels. Additionally, we propose a channel estimation for the IRS-OTFS system where both data and pilots are embedded in the same OTFS frame with minimal guard-band overhead. The paper presents extensive simulation results on the error-rate performance of the proposed system over Rayleigh and Rician fading channels. The effects of the number of IRS elements, the phase selections, and the channel estimation method on the system's performance are investigated. The comprehensive simulation study demonstrates that by exploiting the features of both OTFS and IRS, the proposed approach can provide impressive performance in high Doppler environments.

Underwater VLC channel estimation based on Kalman filtering for direct current optical- and asymmetrically clipping optical- orthogonal frequency division multiplexing techniques

In this paper, we suppose an underwater optical wireless communication (UOWC) system, where the communication utilizes visible light communication for its advantages as wide spectrum, high data rate and high accuracy. The novelty of this paper is focused on improving the channel estimation between transmitter and receiver, where using Kalman Filter (KF) for channel estimation in UOWCs achieves the best results as compared to other traditional channel estimation methods. The scenario of this paper is summarized in transmitting data from transmitter to receiver via underwater harbor and coastal channels. Two channel models are utilized: weighted double gamma functions (WDGF) and a combination of exponential and arbitrary power function (CEAPF). The modulation technique used is optical orthogonal frequency division multiplexing with two kinds: direct current optical orthogonal frequency division multiplexing (DCO-OFDM) and asymmetrically clipping optical orthogonal frequency division multiplexing (ACO-OFDM). Three different techniques are used for channel estimation: Least Square (LS), minimum mean square error (MMSE), and KF. The simulation results reveal that the ACO-OFDM modulation technique with CEAPF channel modeling using KF achieves the lowest bit error rate (BER) compared to other channel estimation methods. The improvement percentage at BER = 10−1 is 13.3% for ACO-OFDM over DCO-OFDM with CEAPF in coastal water and is is 9.3% for WDGF. This indicates that CEAPF performs about 4% better than WDGF for ACO-OFDM than DCO-OFDM in terms of channel estimation.

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 Model-Driven Channel Estimation Method for Millimeter-Wave Massive MIMO Systems

Aiming at the problem of low estimation accuracy under a low signal-to-noise ratio due to the failure to consider the "beam squint" effect in millimeter-wave broadband systems, this paper proposes a model-driven channel estimation method for millimeter-wave massive MIMO broadband systems. This method considers the "beam squint" effect and applies the iterative shrinkage threshold algorithm to the deep iterative network. First, the millimeter-wave channel matrix is transformed into a transform domain with sparse features through training data learning to obtain a sparse matrix. Secondly, a contraction threshold network based on an attention mechanism is proposed in the phase of beam domain denoising. The network selects a set of optimal thresholds according to feature adaptation, which can be applied to different signal-to-noise ratios to achieve a better denoising effect. Finally, the residual network and the shrinkage threshold network are jointly optimized to accelerate the convergence speed of the network. The simulation results show that the convergence speed is increased by 10% and the channel estimation accuracy is increased by 17.28% on average under different signal-to-noise ratios.

Low PAPR preamble-based channel estimation for OTFS systems on static multipath channels

In this paper we propose two preamble-based channel estimation methods for orthogonal time frequency space (OTFS) modulation. These new methods allow a robust channel estimation in noisy scenarios and static multipath channels. Moreover, the proposed preambles exhibit a strong resilience to nonlinearities introduced by high power amplifiers (HPA). We demonstrate that our proposed methods outperform the existing techniques in the literature, allowing the HPA to work at a low input backoff (IBO). To the best of the authors' knowledge, this is the first paper that addresses a study of HPA linearity in OTFS systems. Finally, we show that the complexity of our channel estimators is low enough for an efficient hardware implementation.

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.

GFDM TRANSCEIVER BASED ON ANN CHANNEL ESTIMATION

Generalized frequency division multiplexing (GFDM) is one of the candidate waveforms beyond 5G for wireless communication. Channel estimation is challenging in wireless transmission because of inherent interference. It is based on a pilot signal that uses Least Square (LS) and Normalized Least Mean Square (NLMS) to perform the estimation. This paper used the Artificial Neural Network (ANN) as a channel estimation method, considered a novel estimation process for the GFDM transceiver. The channel estimation based on ANN depends on the data set generated from LS estimation, which considers the proposed method is optimized to LS based on the backpropagation neural network. The ANN algorithm considered a fitness function to estimate the channel. Levenberg-Marquardt backpropagation (LM), Bayesian Regularization backpropagation (BR), and Scaled Conjugate Gradient backpropagation (SCG) training methods training by using the Matlab toolbox used to perform the estimation. BR training method gives the best performance than LS and other training methods at 22 neurons in hidden layers, which at 20dB give BER = 0.0369 that enhanced over LS by 0.08.

Rate-Region Characterization and Channel Estimation for Cell-Free Symbiotic Radio Communications

Cell-free massive MIMO and symbiotic radio communication have been recently proposed as the promising beyond fifth-generation (B5G) networking architecture and transmission technology, respectively. To reap the benefits of both, this paper studies cell-free symbiotic radio communication systems, where a number of cell-free access points (APs) cooperatively send primary information to a receiver, and simultaneously support the passive backscattering communication of the secondary backscatter device (BD). We first derive the achievable communication rates of the active primary user and passive secondary user under the assumption of perfect channel state information (CSI), based on which the transmit beamforming of the cell-free APs is optimized to characterize the achievable rate-region of cell-free symbiotic communication systems. Furthermore, to practically acquire the CSI of the active and passive channels, we propose an efficient channel estimation method based on two-phase uplink-training, and the achievable rate-region taking into account CSI estimation errors is further characterized. Simulation results are provided to show the effectiveness of our proposed beamforming and channel estimation methods.

Message Passing-Aided Joint Data Detection and Estimation of Nonlinear Satellite Channels

Satellite communication is capable of supporting seamless global coverage. However, owing to the reliance on limited-duration solar power, the high power amplifier (HPA) is often driven close to its saturation point, which leads to severe nonlinear distortion in satellite channels. Thus, mitigating the effect of the nonlinear distortion becomes essential for reliable communications. In this article, we propose an efficient joint channel estimation and data detection method based on message passing within the associated factor graph modelling the HPA employed in nonlinear satellite channels. Then, we develop a combined belief propagation and mean field (BP-MF) method to cope with the hard constraints and dense short loops on the factor graph. In particular, the parametric message updating expressions relying on the canonical parameters are derived in the symbol detection part. To alleviate the impact of dense loops, we reformulate the system model into a compact form within the channel estimation part and then reconstruct a loop-free subgraph associated with vector-valued nodes to guarantee convergence. Furthermore, the proposed BP-MF method is also extended to the realistic scenario of having unknown noise variance. To further reduce the computational complexity of the large-scale matrix inversion of channel estimation, the generalized approximate message passing (GAMP) algorithm is employed to decouple the vector of channel coefficient estimation into a series of scalar estimations. Simulation results show that the proposed methods outperform the state-of-the-art benchmarks both in terms of bit error rate performance and channel estimation accuracy.

Fluid Antenna With Linear MMSE Channel Estimation for Large-Scale Cellular Networks

The concept of reconfigurable fluid antennas (FA) is a potential and promising solution to enhance the spectral efficiency of wireless communication networks. Despite their many advantages, FA-enabled communications have limitations as they require an enormous amount of spectral resources in order to select the most desirable position of the radiating element from a large number of prescribed locations. In this paper, we present an analytical framework for the outage performance of large-scale FA-enabled communications, where all user equipments (UEs) employ circular multi-FA array. In contrast to existing studies, which assume perfect channel state information, the developed framework accurately captures the channel estimation errors on the performance of the considered network deployments. In particular, we focus on the limited coherence interval scenario, where a novel sequential linear minimum mean-squared error (LMMSE)-based channel estimation method is performed for only a very small number of FA ports. Next, for the communication of each BS with its associated UE, a low-complexity port-selection technique is employed, where the port that provides the highest signal-to-interference-plus-noise-ratio is selected among the ports that are estimated to provide the strongest channel from each FA. By using stochastic geometry tools, we derive both analytical and closed-form expressions for the outage probability, highlighting the impact of channel estimation on the performance of FA-based UEs. Our results reveal the trade-off imposed between improving the network’s performance and reducing the channel estimation quality, indicating new insights for the design of FA-enabled communications.

A Multi-Resolution Channel Structure Learning Estimation Method of Geometry-Based Stochastic Model With Multi-Scene

Vehicle-to-vehicle (V2V) communication is the most typical application of the Internet of Vehicles, and it has many envisaged applications, such as driverless driving, collision warning and addressing traffic congestion. The realization of future intelligent transportation systems (ITSs) will require V2V communication technology, which in turn relies on accurate V2V channel estimation. As the possibilities that 5G technologies can be used for V2V communications are increasingly being explored, corresponding channel models used to describe the characteristics of V2V channels are also being updated, so the new channel estimation algorithms needed to be developed. Although the channel estimation method based on Convolutional Neural Network (CNN) has achieved remarkable success in communication problems in recent years, it cannot be well adapted to V2V channels under different scenarios and different modeling methods. In this article, a method is proposed for learning and estimating the channel structure in the 5G NR downlink that can adapt to the V2V channel under multi-scenes. The inherent block sparsity characteristics of the channel structure is adopted to combine the group lasso Alternating Direction Method of Multipliers (ADMM) algorithm with CNN, and residual dense network is employed to estimate and refine the V2V channel structure. Furthermore, the classifier and interpolation with pooling method are used to estimate the V2V channel structure under multi-scene and multi-resolution. Simulation results show that the performance of this method is better than other deep learning based estimation algorithms.