Time-varying Channel Estimation Research Articles

Time-Varying Channel Estimation Based on Distributed Compressed Sensing for OFDM Systems.

For orthogonal frequency division multiplexing (OFDM) systems in high-mobility scenarios, the estimation of time-varying multipath channels not only has a large error, which affects system performance, but also requires plenty of pilots, resulting in low spectral efficiency. To address these issues, we propose a time-varying multipath channel estimation method based on distributed compressed sensing and a multi-symbol complex exponential basis expansion model (MS-CE-BEM) by exploiting the temporal correlation and the joint delay sparsity of wideband wireless channels within the duration of multiple OFDM symbols. Furthermore, in the proposed method, a sparse pilot pattern with the self-cancellation of pilot intercarrier interference (ICI) is adopted to reduce the input parameter error of the MS-CE-BEM, and a symmetrical extension technique is introduced to reduce the modeling error. Simulation results show that, compared with existing methods, this proposed method has superior performances in channel estimation and spectrum utilization for sparse time-varying channels.

Time-Varying Channel and Intrablock Carrier Frequency Offset Estimation for OFDM Underwater Acoustic Communication

Time-Varying Channel and Intrablock Carrier Frequency Offset Estimation for OFDM Underwater Acoustic Communication

Joint DFO and channel estimation scheme for RIS assisted OFDM systems

In the reconfigurable intelligent surface (RIS) assisted mobile wireless communications, the performance gains provided by RIS cannot be achieved without the accurate channel estimation. Many time-varying channel estimation algorithms have been proposed for such systems, but none of them consider the effect of Doppler frequency offset (DFO) synchronization error, which can seriously degrade the accuracy of channel estimation, especially in the Rician fading channel for the orthogonal frequency division multiplexing (OFDM) systems. In our work, the effect of DFO synchronization error on the time-varying channel estimation is firstly analyzed, and then a deep learning based joint DFO and channel estimation scheme is proposed. Specifically, the threshold-based noise reduction algorithm and the dual convolution neural networks (CNN) are applied for improve the estimation accuracy. To enhance the practicability of the proposed method, the training target of CNN network is set as the actual estimation value with high accuracy, rather than the ideal information. The simulation results show that the proposed method exhibits superior performance compared to the existing algorithms, and it has an acceptable computational complexity.

Generalized spectrum analysis of Chirp Cyclostationary signals associate with linear canonical transform

A chirp cyclostationary (CCS) process is a nonstationary stochastic process, whose statistics have been defined and utilized in radar and communication signal processing. However, spectrum analysis of CCS signals, which provides a spectral view to characterize a signal, has not been performed. Classical spectrum analysis is based on the Fourier transform, which is not suitable for CCS signals because of the multiplicative chirp signals in a CCS model. In this study, we propose a generalized spectral representation of chirp cyclostationary signals associated with a linear canonical transform. The proposed representation shows that a CCS signal can be represented by a linear combination of a series of jointly stationary and equal-bandwidth processes, which bridge the nonstationary and stationary processes. Furthermore, the relationships between the second-order statistics of the CCS process and its stationary representations are introduced. Finally, based on the above observations, a linear time-varying Wiener filter was designed for CCS signal denoising and time-varying channel estimation. Finally, the relationships between the nonstationarity of the input and output and time-varying properties of the systems are discussed, which are helpful for determining the nonstationarity of the outputs or the time-varying property of a system.

On the Doppler Squint Effect in OTFS Systems Over Doubly-Dispersive Channels: Modeling and Evaluation

Extensive work has demonstrated the excellent performance of orthogonal time frequency space (OTFS) modulation in high-mobility scenarios. Time-variant wideband channel estimation serves as one of the key compositions of OTFS receivers since the data detection requires accurate channel state information (CSI). In practical wideband OTFS systems, the Doppler shift brought by the high mobility is frequency-dependent, which is referred to as the Doppler Squint Effect (DSE). Unfortunately, DSE was ignored in overall prior estimation schemes employed in OTFS systems, which leads to severe performance loss in channel estimation and the consequent data detection. In this paper, we investigate DSE of wideband time-variant channel in delay-Doppler domain and concentrate on the characterization of OTFS channel coefficients considering DSE. The formulation and evaluation of OTFS input-output relationship are provided for both ideal and rectangular waveforms considering DSE. The channel estimation is therefore formulated as a sparse signal recovery problem and an orthogonal matching pursuit (OMP)-based scheme is adopted to solve it. Simulation results confirm the significance of DSE and the performance superiority compared with traditional channel estimation approaches ignoring DSE.

Time-Varying Channel Estimation and Symbol Detection for Underwater Acoustic FBMC-OQAM Communications

Time-Varying Channel Estimation and Symbol Detection for Underwater Acoustic FBMC-OQAM Communications

Active 3D Double-RIS-Aided Multi-User Communications: Two-Timescale-Based Separate Channel Estimation via Bayesian Learning

Double-reconfigurable intelligent surface (RIS) is a promising technique, achieving a substantial gain improvement compared to single-RIS techniques. However, in double-RIS-aided systems, accurate channel estimation is more challenging than in single-RIS-aided systems. This work solves the problem of double-RIS-based channel estimation based on active RIS architectures with only one radio frequency (RF) chain. Since the slow time-varying channels, i.e., the BS-RIS 1, BS-RIS 2, and RIS 1-RIS 2 channels, can be obtained with active RIS architectures, a novel multi-user two-timescale channel estimation protocol is proposed to minimize the pilot overhead. First, we propose an uplink training scheme for slow time-varying channel estimation, which can effectively address the double-reflection channel estimation problem. With channels’ sparisty, a low-complexity Singular Value Decomposition Multiple Measurement Vector-Based Compressive Sensing (SVD-MMV-CS) framework with the line-of-sight (LoS)-aided off-grid MMV expectation maximization-based generalized approximate message passing (M-EM-GAMP) algorithm is proposed for channel parameter recovery. For fast time-varying channel estimation, based on the estimated large-timescale channels, a measurements-augmentation-estimate (MAE) framework is developed to decrease the pilot overhead. Additionally, a comprehensive analysis of pilot overhead and computing complexity is conducted. Finally, the simulation results demonstrate the effectiveness of our proposed multi-user two-timescale estimation strategy and the low-complexity Bayesian CS framework.

Multi-antenna GFDM for resilient decentralized communications

Owing to the superior performance of generalized frequency division multiplexing (GFDM), in terms of enhanced spectral efficiency and lower out-of-band radiations, it is considered as a potential replacement of traditional orthogonal frequency division multiplexing (OFDM) for next generation wireless communication systems. However, non-orthogonal pulse shaping in GFDM gives rise to intrinsic self-interference complicating the receiver design. Moreover, its extension to multi-input multi-output (MIMO) designs for spatial diversity and enhanced reliability is also not straightforward as overlapping of transmitted symbols in time and frequency hinders the extension of conventional diversity techniques to MIMO GFDM. In this work, we consider a multi-antenna GFDM decentralized communication system and present a generic framework to achieve spatial transmit diversity along with a low complexity transceiver design. We extend our proposal by presenting a novel multi-antenna preamble that helps not only in acquisition of robust time and frequency synchronization but also in reliable estimation of time-varying frequency selective channels. Performance evaluation over realistic 3GPP simulation scenarios, confirms the attainment of full diversity order along with superior preamble-based time–frequency synchronization and channel estimation performance as compared to state of the art.

Dynamic channel estimation-aware routing protocol in mobile cognitive radio networks for smart IIoT applications

Cognitive Radio Networks (CRNs) have become a successful platform in recent years for a diverse range of future systems, in particularly, industrial internet of things (IIoT) applications. In order to provide an efficient connection among IIoT devices, CRNs enhance spectrum utilization by using licensed spectrum. However, the routing protocol in these networks is considered one of the main problems due to node mobility and time-variant channel selection. Specifically, the channel selection for routing protocol is indispensable in CRNs to provide an adequate adaptation to the Primary User (PU) activity and create a robust routing path. This study aims to construct a robust routing path by minimizing PU interference and routing delay to maximize throughput within the IIoT domain. Thus, a generic routing framework from a cross-layer perspective is investigated that intends to share the information resources by exploiting a recently proposed method, namely, Channel Availability Probability. Moreover, a novel cross-layer-oriented routing protocol is proposed by using a time-variant channel estimation technique. This protocol combines lower layer (Physical layer and Data Link layer) sensing that is derived from the channel estimation model. Also, it periodically updates and stores the routing table for optimal route decision-making. Moreover, in order to achieve higher throughput and lower delay, a new routing metric is presented. To evaluate the performance of the proposed protocol, network simulations have been conducted and also compared to the widely used routing protocols, as a benchmark. The simulation results of different routing scenarios demonstrate that our proposed solution outperforms the existing protocols in terms of the standard network performance metrics involving packet delivery ratio (with an improved margin of around 5–20% approximately) under varying numbers of PUs and cognitive users in Mobile Cognitive Radio Networks (MCRNs). Moreover, the cross-layer routing protocol successfully achieves high routing performance in finding a robust route, selecting the high channel stability, and reducing the probability of PU interference for continued communication.

Estimation of Time-Varying Channels in Virtual Angular Domain for Massive MIMO Systems

This paper is concerned with the problem of channel estimation in virtual angular domain in massive MIMO orthogonal frequency division multiplexing (OFDM) systems, in the presence of time varying channels. The proposed channel estimator is based on dichotomous coordinate decent joint sparse recovery (DCD-JSR) algorithm, which was well suited for massive MIMO systems with channels static over several OFDM symbols, but it is not accurate for time-varying channels. The estimator proposed in this paper exploits the basis expansion model (BEM) for approximation of the time-variation of the channel and the sparsity of the channel in the virtual angular domain with a common support over OFDM subcarriers and OFDM symbols for joint estimation of the BEM expansion coefficients. It is shown that, when using the Legendre polynomials as the BEM and depending on the normalized Doppler frequency, only a small number of expansion coefficients is required to provide accurate channel estimation.

Deep learning assisted time-varying channel estimation in multi-user mmWave hybrid MIMO systems

Hybrid analog/digital multiple input multiple output (MIMO) system is proposed to mitigate the challenges of millimeter wave (mmWave) communication. This architecture enables utilizing the large array gain with reasonable power consumption. However, new methods are required for the channel estimation problem of hybrid architecture-based systems due to the fewer number of radio frequency (RF) chains than antenna elements. Leveraging the sparse nature of the mmWave channels, compressed sensing (CS)-based channel estimation methods are proposed. Recently, machine learning (ML)-aided methods have been investigated to improve the channel estimation performance. Additionally, the Doppler effect should be considered for the high mobility scenarios, and we deal with the time-varying channel model. Therefore, in this article, we consider the scenario of time-varying channels for a multi-user mmWave hybrid MIMO system. By proposing a Deep Neural Network (DNN) and defining the inputs and outputs, we introduce a novel algorithm called Deep Learning Assisted Angle Estimation (DLA-AE) for improving the estimation of the Angles of Departure/Arrival (AoDs/AoAs) of the channel paths. In addition, we suggest Linear Phase Interpolation (LPI) to acquire the path gains for the data transmission instants. Simulation results show that utilizing the proposed DLA-AE and LPI methods enhance the time-varying channel estimation accuracy with low computational complexity.

Time-Varying Channel Estimation Scheme for Uplink MU-MIMO in 6G Systems

Channel estimation is a challenging issue for millimeter wave (mmWave) and massive multiple-input multiple-output (MIMO) in the future sixth generation (6G) wireless systems, where the conventional estimation schemes may fail to track the fast varying channels, especially in high-speed mobile scenarios. In this paper, a novel tensor-based uplink channel estimation scheme is proposed for multi-user MIMO (MU-MIMO) systems over time-varying channels. In the proposed scheme, a low-overhead pilot transmission scheme is designed to track the varying channel. The received uplink signal at the base station (BS) is formulated as a third-order tensor which admits a CANDECOMP/PARAFAC (CP) model. The CP decomposition issue is then solved using blind matrix decomposition, in which the special structures of signals in the time dimension and the matrix subspace are utilized. By exploiting low-rank structure of the signal tensor, the channel parameters (angles of arrival/departure, path gains, and Doppler shifts) are estimated from the factor matrices. Moreover, the proposed scheme is theoretically analyzed and it is guaranteed with low pilot overhead. Simulation results verify that the proposed scheme can outperform the compressed sensing (CS) based scheme and the iteration-based scheme in terms of accuracy and stability. The uniqueness of the proposed scheme is also verified in our simulation.

Joint fast time-varying channel estimation with noise elimination and ICI cancellation for LTE-V2X systems

Vehicle-to-everything (V2X) communication is essential for intelligent transportation systems (ITS) and critical technology to ensure traffic safety. Aiming at the problems of noise interference, time-varying, and inter-carrier interference (ICI) in the LTE-V2X channel, a joint fast time-varying channel estimation with noise elimination and ICI cancellation is proposed in this paper. Firstly, using the autocorrelation characteristics of the Zadoff Chu (ZC) pilot sequence, a modified discrete Fourier transform (M-DFT) channel estimation algorithm is proposed to eliminate the noise in cyclic prefix (CP). Secondly, a joint iterative direct decision (IDD) and time-varying channel fitting (CF), called IDD-CF channel estimation algorithm, is proposed to track the rapid changes of channels on different symbols and eliminate ICI. The system simulation results show that the proposed joint fast time-varying channel estimation algorithm can effectively eliminate the noise and ICI, improve the performance of channel estimation, and have better robustness under different Doppler frequency shifts than the representative channel estimation algorithm.

A Compressive Sensing and Deep Learning-Based Time-Varying Channel Estimation for FDD Massive MIMO Systems

To achieve the performance gains of massive multiple-input multiple-output (MIMO) systems, the downlink channel state information (CSI) must be acquired at the base station (BS). In frequency division duplexing (FDD) massive MIMO systems, the BS always first transmits downlink pilot symbols so that the user equipment (UE) can estimate CSI and then feedback to the BS. However, the huge number of antennas at the BS will lead to overwhelming feedback overhead. Moreover, time-varying caused by high mobility of user terminals makes the priori channel knowledge of the channels to change from one slot to another so that CSI aquisition is hard. To simultaneously reduce the overhead of overwhelming downlink pilot signaling and uplink feedback in time-varying massive MIMO systems, we propose a channel estimation scheme based on compressive sensing (CS) and deep learning (DL) in frequency division duplexing (FDD) massive MIMO systems. Specifically, we first develop a new CS-based algorithm for sparse channel estimation, which requires no priori knowledge of channel statistics. After obtaining the innitial channel estimation, we utilize two DL-based networks, named DnNet and DnLSTM respectively for denoising. Simulation results demonstrate that the proposed method can considerably reduce the training and feedback overhead and outperform the existing classical algorithms.

High-Mobility Satellite-UAV Communications: Challenges, Solutions, and Future Research Trends

Due to their fast deployment and high flexibility, unmanned aerial vehicles (UAVs) are regarded as vital enablers of various applications that include emergency rescue, telecommunications, delivery of supplies, and more. In order to connect UAVs to a core network anywhere, satellite communications is expected to provide wider backhaul coverage compared to ground wireless communications. Although UAV Communications has been studied in many technical works, high-mobility satellite-UAV communications has not been investigated well. Specifically, physical layer operations of the high-mobility satellite-UAV communication system are affected the most by high-mobility environments. In this article, we focus on the physical layer operation problem in satellite-UAV communications from three perspectives: time-varying channel estimation, the high-mobility transceiver, and high-mobility multiple access. Related key technologies, existing works, open problems, and the next development direction are introduced and surveyed. Furthermore, our proposed scheme is also discussed. Overall, this article aims to help readers understand challenges, solutions, and future research trends regarding physical layer operation issues on high-mobility satellite-UAV aerial communications.

Time‐Varying Channel Estimation Based on Air‐Ground Channel Modelling and Modulated Learning Networks

To improve the time-varying channel estimation accuracy of orthogonal frequency division multiplexing air-ground datalink in complex environment, this paper proposes a time-varying air-ground channel estimation algorithm based on the modulated learning networks, termed as MB-ChanEst-TV. The algorithm integrates the modulated convolutional neural networks (MCNN) with the bidirectional long short term memory (Bi-LSTM), where the MCNN subnetworks accomplish channel interpolation in frequency domain and compress the network model while the Bi-LSTM subnetworks achieve channel prediction in time domain. Considering the unique characteristics of airframe shadowing for unmanned aircraft systems, we propose to combine the classical 2-ray channel model with the tapped delay line model and present a more realistic channel impulse response samples generation approach, whose code and dataset have been made publicly available. We demonstrate the effectiveness of our proposed approach on the generated dataset, where experimental results indicate that the MB-ChanEst-TV model outperforms existing state-of-the-art methods with a lower estimation error and better bit error ratio performance under different signal to noise ratio conditions. We also analyze the effect of roll angle of the aircraft and the duration percentage of the airframe shadow on the channel estimation.

Performance analysis of the low‐complexity adaptive channel estimation algorithms over non‐stationary multipath Rayleigh fading channels under carrier frequency offsets

The use of selective partial updating method for reducing computational complexity of adaptive algorithms has been proposed recently. However, its performance analysis has not been studied as extensively and exactly as LMS or RLS algorithms for time-varying channel estimation. This paper provides performance analysis of the low-complexity family of affine projection algorithms based on selective partial update method on the estimation of multipath Rayleigh fading channels in the presence of carrier frequency offsets (CFO) and random channel variations. The analysis is based on the calculation of the error correlation matrix of the estimation, the mean-square weight error (MSWE) and the mean-square estimation error (MSE) parameters. The analysis does not use strong assumptions like small or large step-size, and explicit closed-form expressions for the MSE of estimation are obtained only from common hypotheses in wireless communication context. In this paper, the optimum step-size parameters minimizing the MSE of estimation are analytically derived without any simplifying assumptions. For the sake of comparison with other analytical approaches, the performance of the introduced algorithms is also investigated using the energy conservation relation. Likewise, for exact performance analysis, all the moment terms that appear in closed form expressions for the MSE of estimation are evaluated. Simulations are conduced to corroborate the presented studies and show that the theoretical results agree well with the simulation results over non-stationary multipath Rayleigh fading channels.

Accurate Channel Estimation and Adaptive Underwater Acoustic Communications Based on Gaussian Likelihood and Constellation Aggregation

Achieving accurate channel estimation and adaptive communications with moving transceivers is challenging due to rapid changes in the underwater acoustic channels. We achieve an accurate channel estimation of fast time-varying underwater acoustic channels by using the superimposed training scheme with a powerful channel estimation algorithm and turbo equalization, where the training sequence and the symbol sequence are linearly superimposed. To realize this, we develop a ‘global’ channel estimation algorithm based on Gaussian likelihood, where the channel correlation between (among) the segments is fully exploited by using the product of the Gaussian probability-density functions of the segments, thereby realizing an ideal channel estimation of each segment. Moreover, the Gaussian-likelihood-based channel estimation is embedded in turbo equalization, where the information exchange between the equalizer and the decoder is carried out in an iterative manner to achieve an accurate channel estimation of each segment. In addition, an adaptive communication algorithm based on constellation aggregation is proposed to resist the severe fast time-varying multipath interference and environmental noise, where the encoding rate is automatically determined for reliable underwater acoustic communications according to the constellation aggregation degree of equalization results. Field experiments with moving transceivers (the communication distance was approximately 5.5 km) were carried out in the Yellow Sea in 2021, and the experimental results verify the effectiveness of the two proposed algorithms.

Multi Stage Kalman Filter (MSKF) Based Time-Varying Sparse Channel Estimation With Fast Convergence

Multi Stage Kalman Filter (MSKF) Based Time-Varying Sparse Channel Estimation With Fast Convergence

Deep Learning-Based Time-Varying Channel Estimation for RIS Assisted Communication

Reconfigurable intelligent surface (RIS) is considered as a revolutionary technology for future wireless communication networks. In this letter, we consider the acquisition of the time-varying cascaded channels, which is a challenging task due to the massive number of passive RIS elements and the small channel coherence time. To reduce the pilot overhead, a deep learning-based channel extrapolation is implemented over both antenna and time domains. We divide the neural network into two parts, i.e., the time-domain and the antenna-domain extrapolation networks, where the neural ordinary differential equations (ODE) are utilized. In the former, ODE accurately describes the dynamics of the RIS channels and improves the recurrent neural network’s performance of time series reconstruction. In the latter, ODE is resorted to modify the relations among different data layers in a feedforward neural network. We cascade the two networks and jointly train them. Simulation results show that the proposed scheme can effectively extrapolate the cascaded RIS channels in high mobility scenario.