Conventional Channel Estimation Schemes Research Articles

Parametric Sparse Channel Estimation for RIS-Assisted Terahertz Systems

To support extremely high data rates in 6G wireless networks, reconfigurable intelligent surface (RIS)-assisted terahertz (THz) communications have gained much attention in recent years. By manipulating the phase shifts of reflecting elements, the RIS can proactively adjust the wireless propagation environment of THz systems, thereby enhancing the overall throughput significantly. To realize the full potential of RIS-assisted THz systems, an acquisition of accurate channel information is of great importance. However, since the wavefront of the THz electromagnetic signal is spherical, the conventional channel estimation techniques using the planar wavefront assumption suffer from severe performance degradation in the near-field RIS-assisted THz systems. An aim of this work is to propose an efficient channel estimation technique for near-field RIS-assisted wideband THz systems. Key idea of the proposed polar-domain frequency-dependent RIS-assisted channel estimation (PF-RCE) scheme is to estimate the sparse multipath components (i.e., angles, distances, and path gains) of the near-field THz channel by exploiting the polar-domain sparsity and common support properties. We demonstrate from the numerical evaluations that PF-RCE achieves a significant performance gain over the conventional THz channel estimation schemes in terms of the normalized mean square error (NMSE).

Multi-Task Learning-Based Channel Estimation for RIS Assisted Multi-User Communication Systems

In this letter, we propose a multi-task learning (MTL)-based joint channel estimation scheme for reconfigurable intelligent surface (RIS) assisted millimeter-wave communication system, where the direct channel (DC) and cascaded channel (CC) are estimated at the same coherence time by learning the feature of shared pilots. Since the dimension of CC is much larger than the DC, we design a learnable joint loss function based on homoscedastic task uncertainty to balance the training of two subtasks. Meanwhile, the residual shrinkage blocks are introduced into the multi-task network architecture to release the noise effect. Simulation results show that the estimation accuracy of MTL with less pilot overhead outperforms conventional channel estimation scheme, and significantly reduces training overhead compared with the single-task network.

Time-Domain Channel Estimation Scheme for OFDM over Fast Fading Channels

In high-mobility scenarios, the time variation of mobile radio channels leads to a loss of orthogonality among subcarriers in orthogonal frequency division multiplexing (OFDM) systems, resulting in intercarrier interference (ICI) and performance deterioration. Conventional channel estimation schemes are usually based on pilot tones, which are distributed in each OFDM symbol to estimate the channel variation. Hence, the channel estimator itself suffers from ICI. In this study, a new estimation scheme, which does not suffer from ICI, is proposed to estimate the channel variation within OFDM symbols. The main idea is to zero-pad (ZP) the OFDM symbol in the time domain. Then, in the middle of the ZP interval, an impulse signal is inserted as a pilot sample, which is used to estimate the channel at the pilot signal in the OFDM symbol. Finally, a linear model is used to estimate the channel variation over an OFDM symbol. Additionally, we derive the mean squared error (MSE) of the proposed estimation technique under the constraint that the channel varies linearly within OFDM symbols. Simulation results show that our scheme can achieve a substantial improvement in the bit error rate (BER) performance of OFDM, in spite of the OFDM symbol length being increased. Moreover, in many cases, the new scheme can achieve the same BER performance as the perfect knowledge of channel state information (CSI). Theoretical analysis and numerical simulations show that our scheme achieves excellent performance with much lower computational complexity.

Data-Aided Channel Estimation for OTFS Systems With a Superimposed Pilot and Data Transmission Scheme

The recently developed orthogonal time frequency space (OTFS) modulation has shown its capability of coping with the fast time-varying channels in high-mobility environments. In particular, OTFS modulation gives rise to the sparse representation of the delay-Doppler (DD) domain channel model. Hence, one can an enjoy accurate channel estimation by adopting only one pilot symbol. However, conventional OTFS channel estimation schemes require the deployment of guard space to avoid data-pilot interference, which inevitably sacrifices the spectral efficiency. In this letter, we develop a data-aided channel estimation algorithm for a superimposed pilot and data transmission scheme, which can improve the spectral efficiency. To accurately estimate the channel and detect the data symbols, we coarsely estimate the channel based on the pilot symbol, followed by an iterative process which detects the data symbols and refines the channel estimates. Simulation results show that the bit error rate (BER) performance based on the proposed method can approach the baseline scheme with perfect channel estimation.

Channel estimation for reconfigurable intelligent surface assisted wireless communication systems in mobility scenarios

Reconfigurable intelligent surface (RIS) can manipulate the wireless propagation environment by smartly adjusting the amplitude/phase in a programmable panel, enjoying the improved performance. The accurate acquisition of the instantaneous channel state information (CSI) in the cascaded RIS chain makes an indispensable contribution to the performance gains. However, it is quite challenging to estimate the CSI in a time-variant scenario due to the limited signal processing capability of the passive elements embedded in a RIS pannel. In this work, a channel estimation scheme for the RIS-assisted wireless communication system is proposed, which is demonstrated to perform well in a time-variant scenario. The cascaded RIS channel is modeled as a state-space model based upon the mobility situations. In addition, to fully exploit the time correlation of channel, Kalman filter is employed by taking the prior information of channels into account. Further, the optimal reflection coefficients are derived according to the minimum mean square error (MMSE) criterion. Numerical results show that the proposed methods exhibit superior performance if compared with a conventional channel estimation scheme.

Machine Learning-Based Channel Prediction in Massive MIMO With Channel Aging

To support the ever increasing number of devices in massive multiple-input multiple-output (mMIMO) systems, an excessive amount of overhead is required for conventional orthogonal pilot-based channel estimation schemes. To circumvent this fundamental constraint, we design a machine learning (ML)-based time-division duplex scheme in which channel state information (CSI) can be obtained by leveraging the temporal channel correlation. The presence of the temporal channel correlation is due to the stationarity of the propagation environment across time. The proposed ML-based predictors involve a pattern extraction implemented via a convolutional neural network, and a CSI predictor realized by an autoregressive (AR) predictor or an autoregressive network with exogenous inputs recurrent neural network. Closed-form expressions for the user uplink and downlink achievable spectral efficiency and average per-user throughput are provided for the ML-based time division duplex schemes. Our numerical results demonstrate that the proposed ML-based predictors can remarkably improve the prediction quality for both low and high mobility scenarios, and offer great performance gains on the per-user achievable throughput.

Deep neural network method for channel estimation in visible light communication

Visible light communications (VLC) has been regarded as a promising technology for high-speed indoor wireless accessing since it can offer both lighting and network. However, the spectral efficiency of the VLC system based on orthogonal frequency division multiplexing (OFDM) is always smaller than RF-OFDM because light-emitting diodes (LED) require real-value signals. Pilots occupy the spectrum in proportion for channel estimation(CE) to improve communication quality. Based on this consideration, we firstly present the idea of introducing deep learning technology into the CE scheme in the VLC system and propose a CE scheme based on Deep Neural Networks(DNN) perform as well as conventional CE schemes with fewer pilots. The result of experiments validates the feasibility of DNN-based CE.

WSDSBL Method for Wideband Channel Estimation in Millimeter-Wave MIMO Systems with Lens Antenna Array

In millimeter wave (mmWave) communication systems, Channel State Information(CSI) is extremely essential for beamforming. The traditional Successive Support Detection (SSD) algorithm can achieve high wideband channel estimation accuracy, but it only used least square (LS) algorithm to recover the detected channel part, the estimation accuracy is low under low SNR regions. To tackle this problem, in this paper, inspired by the classic Support Detection (SD) channel estimation scheme in narrowband, we propose an efficient Wideband Support Detection Sparse Bayesian Learning (WSDSBL) channel estimation scheme. For every subcarrier, we first detect the support of the wideband beamspace channel of the subcarrier, then we use the Sparse Bayesian Learning (SBL) scheme to recover it. Simulation results show that the proposed WSDSBL channel estimation algorithm is better than conventional wideband channel estimation schemes in MSE performance and achievable sum-rate performance, especially in low SNR regions.

Deep Learning-Based Channel Estimation for Massive MIMO Systems

In this letter, we propose a deep learning (DL)-based channel estimation scheme for the massive multiple-input multiple-output (MIMO) system. Unlike existing studies, we develop the channel estimation scheme for the case that the pilot length is smaller than the number of transmit antennas. The proposed scheme takes a two-stage estimation process: 1) a DL-based pilot-aided channel estimation and 2) a DL-based data-aided channel estimation. In the first stage, the pilot itself and the channel estimator are jointly designed by using both a two-layer neural network (TNN) and a deep neural network (DNN). In the second stage, the accuracy of channel estimation is further enhanced by using another DNN in an iterative manner. The simulation results demonstrate that the proposed channel estimation scheme has much better performance than the conventional channel estimation scheme. We also derive a useful insight into the optimal pilot length given the number of transmit antennas.

A New Data Pilot-Aided Channel Estimation Scheme for Fast Time-Varying Channels in IEEE 802.11p Systems

The channel estimation in the IEEE 802.11p vehicle-to-everything (V2X) communication systems is generally a challenging task due to high mobility and insufficient number of pilots. Conventional data pilot-aided channel estimation schemes are difficult to apply in practice due to high complexity and the error propagation problem. In this correspondence paper, we propose a new data pilot-aided channel estimation scheme to overcome both issues. To this end, we develop a state feedback decision algorithm that enables us to extract reliable data pilots within a few received symbols. As a result, we can minimize both the error propagation effect and the computational complexity. We verify the complexity gain of the proposed scheme through the complexity analysis. Finally, we demonstrate the accuracy of the proposed channel estimation scheme in terms of the packet error rate performance.

Channel Estimation Scheme in Oversampled Frequency Domain for FBMC-QAM Systems Based on Prototype Filter Set

This paper proposes a channel estimation scheme for the filter-bank multicarrier systems with quadrature amplitude modulation (FBMC-QAM). Under frequency selective channels, the adjacent data symbols cause interference to the pilot symbols in FBMC-QAM systems and degrade the performance of the channel estimation. To handle this problem, we analyze the interference of the pilot symbols in an oversampled frequency domain before the receiver filtering process. Exploiting the interference analysis, we propose a pilot symbol configuration and condition of filter coefficients to cancel the inter-carrier interference. Moreover, we apply a time domain data symbol precoding to eliminate inter-symbol interference. The simulation results show that the proposed scheme outperforms conventional channel estimation schemes.

Cluster-Based Fast Time-Varying MIMO Channel Fading Prediction in the High-Speed Scenario

Accurate channel state information (CSI) is important for the coherent detection of multiple-input-multiple-output (MIMO) system. Especially in a high-speed scenario, fast time-varying CSI gotten by the conventional channel estimation schemes tend to be out of date, thus tracking and predicting CSI are more attractive and indispensable. Motivated by those, a time-varying MIMO channel fading prediction framework is proposed in this paper. The principle behind our scheme is that the cluster-based fading channel has the spatial consistency property, which means the small-scale parameters of channel clusters evolve continuously and smoothly in the time and spatial domains. Thus CSI can be tracked and predicted within several or tens of wavelengths. The proposed scheme is composed of an extended Bayesian Estimation Kalman Filter to track the time-varying CSI evolution, and a Cluster Drifting Based Prediction to obtain the small-scale parameters of channel clusters. The performance of the proposed scheme is simulatively verified by a standard clustered-based channel model.

Novel Interference Cancellation of Channel Estimation Scheme Based on LTE in V2V Communications

Transportation infrastructures integrate advanced Information Technology to enable the operations and management of transportation systems. Advanced vehicle and road systems of a comprehensive concept that improve the efficiency and safety of road traffic are being built and will be commercially available through Intelligent Transport Systems (ITS) technology and services. To support ITS technology, Vehicle-To-Everything (V2X) is needed, and in the 3rd Generation Partnership Project, many studies have focused on Long Term Evolution (LTE)-based vehicle communications. In order to realize reliable and optimized communications performance in vehicle communications, which move in propagation environments at high speed, in this study, we propose a novel channel estimation scheme suited for LTE sidelink-based Vehicle-To-Vehicle systems. Conventional channel estimation schemes can be categorized as Decision-Directed Channel Estimation, spectral temporal averaging, and smoothing methods. In this study, unlike conventional channel estimation schemes, we propose a Novel Interference Cancellation of Channel Estimation (NICCE) using Quadratic Smoothing of the pilot symbols, which estimates a channel with greater accuracy, and a novel interference cancellation of channel estimation in data symbols. In simulation results, the proposed NICCE scheme shows improved overall performance in terms of the Normalized Mean Square Error and uncoded Bit Error Rate relative to conventional schemes.

Adaptive Channel Estimation Scheme Based on LTE Uplink in V2V Environments

Vehicle communication can facilitate efficient coordination among vehicles on the road and enable future vehicular applications such as vehicle safety enhancement, infotainment, or even autonomous driving. In the 3rd Generation Partnership Project (3GPP), many studies focus on long term evolution (LTE)-based vehicle communication. Because vehicle communication is closely related to safety, it requires low latency and improved reliability. However, vehicle speed is high enough to cause severe channel distortion in vehicle-to-vehicle (V2V) environments. We can utilize channel estimation methods to approach a reliable vehicle communication systems. Conventional channel estimation schemes can be categorized as least-squares (LS), decision-directed channel estimation (DDCE), spectral temporal averaging (STA), and smoothing methods. In this study, we propose a smart channel estimation scheme in LTE-based V2V environments. The channel estimation scheme, based on an LTE uplink system, uses a demodulation reference signal (DMRS) as the pilot symbol. Unlike conventional channel estimation schemes, we propose an adaptive smoothing channel estimation scheme (ASCE) using quadratic smoothing (QS) of the pilot symbols, which estimates a channel with greater accuracy and adaptively estimates channels in data symbols. In simulation results, the proposed ASCE scheme shows improved overall performance in terms of the normalized mean square error (NMSE) and bit error rate (BER) relative to conventional schemes.

Blind Channel Estimation for FBMC/OQAM Systems Based on Subspace Approach

The conventional channel estimation schemes for filter bank multicarrier with offset quadrature amplitude modulation (FBMC/OQAM) systems are mainly based on preamble methods. However, the utilization of preamble for channel estimation decreases the system’s spectrum efficiency. In this paper, we propose a modified subspace blind channel estimation method for FBMC/OQAM systems. The proposed method distinguishes itself from previously preamble based methods by utilizing spatial diversity technique to introduce data redundancy for blind channel estimation, which leads to high spectral utilization. Thus, the proposed method can provide significant root mean square error (RMSE) performance improvement compared to conventional preamble based methods at high SNRs. Simulation results verify the validity of the proposed method in FBMC/OQAM systems.

Channel Estimation for mmWave MIMO With Transmitter Hardware Impairments

This letter considers the problem of channel estimation for millimeter wave (mmWave) multiple-input multiple-output systems under a transmitter impairments model. Specifically, taking the transmitter hardware impairments into account, the performance of conventional pilots-based channel estimation scheme will be degraded due to the destroyed training pilots. By exploiting the sparsity of mmWave channel in the angular domain, a new channel estimation algorithm based on the Bayesian compressive sensing (BCS) and least square estimation (LSE) is proposed. First, the expectation maximization algorithm is presented to solve the BCS problem, and the refined measurement matrix and the support of the channel vector are obtained. Next, the channel gain coefficients are estimated by using the LSE. Simulation results show that the proposed algorithm can achieve better performance compared with the conventional BCS and orthogonal matching pursuit algorithm.

Fast Channel Tracking for Terahertz Beamspace Massive MIMO Systems

The recent concept of beamspace multiple input multiple output (MIMO) with discrete lens array can utilize beam selection to reduce the number of radio-frequency chains (RF) required in terahertz (THz) massive MIMO systems. However, to achieve the capacity-approaching performance, beam selection requires information on a beamspace channel of large size. This is difficult to obtain since the user mobility usually leads to the fast variation of THz beamspace channels, and the conventional real-time channel estimation schemes involve unaffordable pilot overhead. To solve this problem, in this paper, we propose the a priori aided (PA) channel tracking scheme. Specifically, by considering a practical user motion model, we first excavate a temporal variation law of the physical direction between the base station and each mobile user. Then, based on this law and the special sparse structure of THz beamspace channels, we propose to utilize the obtained beamspace channels in the previous time slots to predict the prior information of the beamspace channel in the following time slot without channel estimation. Finally, aided by the obtained prior information, the time-varying beamspace channels can be tracked with low pilot overhead. Simulation results verify that to achieve the same accuracy, the proposed PA channel tracking scheme requires much lower pilot overhead and signal-to-noise ratio (SNR) than the conventional schemes.

Estimation of MIMO Channel with Imperfect Channel Correlation Information

Conventional channel estimation schemes may require for the transmission of orthogonal pilot signal for each antenna in frequency-division duplex (FDD) transmission systems. It is of great concern to get channel information with an affordable signaling overhead in FDD massive multi-input multi-output (m-MIMO) transmission environments. The signaling overhead for the channel estimation can be reduced by exploiting the channel correlation matrix (CCM) of m-MIMO channel. When the m-MIMO channel is correlated in spatial domain, the channel information can be estimated with reduced pilot signaling overhead. However, the estimation performance may seriously be affected by the accuracy of CCM. In this paper, we investigate the effect of CCM accuracy on the m-MIMO channel estimation and verify it by computer simulation.

Rateless space–time block code for mitigating pilot contamination effects in multi‐cell massive MIMO system with lossy links

Massive multiple-input multiple-output (MIMO) is a breakthrough wireless transmission technique proposed for future wireless standards due to its spectrum and energy efficiencies. However, pilot contamination constitutes a particularly significant impairment in multi-cell systems that utilise reciprocity-based channel estimation. This phenomenon results from the non-orthogonality of the pilot sequences in different cells. Given a large number of antennas employed in massive MIMO systems, conventional channel estimation schemes cannot mitigate this problem effectively without high computational complexity. In this study, a simple rateless space–time block code (RSTBC) is employed in the downlink transmission of a lossy multi-cell massive MIMO system to mitigate the effects of signal-to-interference-and-noise ratio (SINR) degradation resulting from pilot contamination. Specifically, a robust optimisation problem is formulated to maximise the minimum downlink SINR among all users. Simulation and analytical results for large numbers of antenna elements at the base station (BS) are provided to demonstrate the usefulness of the proposed scheme. The SINR and spectral efficiency analyses indicate that RSTBC can effectively mitigate downlink SINR degradation that is caused by pilot contamination. Experimental results for the proposed scheme are also included for a 2 × 2 MIMO system, and it agrees closely with the simulation.

Joint Channel Training and Feedback for FDD Massive MIMO Systems

Massive multiple-input multiple-output (MIMO) is widely recognized as a promising technology for future 5G wireless communication systems. To achieve the theoretical performance gains in massive MIMO systems, accurate channel state information at the transmitter (CSIT) is crucial. Due to the overwhelming pilot signaling and channel feedback overhead, however, conventional downlink channel estimation and uplink channel feedback schemes might not be suitable for frequency-division duplexing (FDD) massive MIMO systems. In addition, these two topics are usually separately considered in the literature. In this paper, we propose a joint channel training and feedback scheme for FDD massive MIMO systems. Specifically, we firstly exploit the temporal correlation of time-varying channels to propose a differential channel training and feedback scheme, which simultaneously reduces the overhead for downlink training and uplink feedback. We next propose a structured compressive sampling matching pursuit (S-CoSaMP) algorithm to acquire a reliable CSIT by exploiting the structured sparsity of wireless MIMO channels. Simulation results demonstrate that the proposed scheme can achieve substantial reduction in the training and feedback overhead.