Proposed Channel Estimation Scheme Research Articles

Compressive Sensing-Based Channel Estimation for Uplink and Downlink Reconfigurable Intelligent Surface-Aided Millimeter Wave Massive MIMO Systems

This paper investigates single-user uplink and two-user downlink channel estimation in reconfigurable intelligent surface (RIS)-aided millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) wireless communication systems. Because of the difficulty associated with the estimation of channels in RIS-aided wireless communication systems, channel state information (CSI) is assumed to be known at the receiver in some previous works in the literature. By assuming that prior knowledge of the line-of-sight (LoS) channel between the RIS and the base station (BS) is known, two compressive sensing-based channel estimation schemes that are based on simultaneous orthogonal matching pursuit and structured matching pursuit (StrMP) algorithms are proposed for estimation of uplink channel between RIS and user equipment (UE), and joint estimations of downlink channels between BS and a UE, and between RIS and another UE, respectively. The proposed channel estimation schemes exploit the inherent common sparsity shared by the angular domain mmWave channels at different subcarriers. The superiority of one of the proposed channel estimation techniques, the StrMP-based channel estimation technique, with negligibly higher computational complexity cost compared with other channel estimators, is documented through extensive computer simulation. Specifically, with a reduced pilot overhead, the proposed StrMP-based channel estimation scheme exhibits better performance than other channel estimation schemes considered in this paper for signal-to-noise ratio (SNR) between 0 dB and 5 dB upward at different instances for both uplink and downlink scenarios, respectively. However, below these values of SNR the proposed StrMP-based channel estimation scheme will require higher pilot overhead to perform optimally.

Iterative hybrid compressive sensing-based channel estimation method for intelligent reflecting surface-supported millimeter wave systems

Iterative hybrid compressive sensing-based channel estimation method for intelligent reflecting surface-supported millimeter wave systems

Noise-Canceling Channel Estimation Schemes Based on the CIR Length Estimation for IEEE 802.11p/OFDM Systems

This paper investigates methods for noise-canceling channel estimation (NC-CE) to track rapid time-varying channels in IEEE 802.11p/orthogonal frequency division multiplexing (OFDM) systems. To this end, we introduce a novel three-step channel estimation technique based on the estimated length of the channel impulse response (CIR). This approach aims to surpass the performance of conventional designs that rely on constructed data pilots (CDPs). In the first step, we not only eliminate noise components but also estimate the channel frequency responses (CFRs) of virtual subcarriers for long preamble parts. Moving on to the second step, we incorporate a modified CDP method without a frequency-domain reliability test and interpolation, taking into account the CFRs of virtual subcarriers obtained at the previous OFDM symbol time. The final step can be implemented as the operation of the inverse fast Fourier transform (IFFT)/nulling/FFT to reduce noise components from the CFRs obtained in the second step and generate CFRs for virtual subcarriers to be used in the next symbol time. The results of our simulations validate the effectiveness of our proposed channel estimation schemes.

Sensing User's Channel and Location With Terahertz Extra-Large Reconfigurable Intelligent Surface Under Hybrid-Field Beam Squint Effect

This paper investigates the sensing of user's uplink channel and location in terahertz extra-large reconfigurable intelligent surface (XL-RIS) systems, where the unique hybrid far-near field effect and the beam squint effect caused by the XL array aperture as well as the XL bandwidth are overcome. Specifically, we first propose a joint channel and location sensing scheme, which consists of a location-assisted generalized multiple measurement vector orthogonal matching pursuit (LA-GMMV-OMP) algorithm for channel estimation (CE) and a complete dictionary based localization (CDL) scheme, where a frequency selective polar-domain redundant dictionary is proposed to overcome the hybrid field beam squint effect. The CE module outputs coarse on-grid angle estimation (respectively observed from the BS and RIS) to the localization module, which returns the fine off-grid angle estimation to improve CE. Particularly, with RIS, CDL can obtain user's location via line intersection, and a polar-domain gradient descent (PGD) algorithm at the base station is proposed to achieve the off-grid angle estimation with super-resolution accuracy. Additionally, to further reduce the sensing overhead, we propose a partial dictionary-based localization scheme, which is decoupled from CE, where RIS is served as an anchor to lock the user on the hyperbola according to time difference of arrival and the user's off-grid location can be obtained by using the proposed PGD algorithm. Simulation results demonstrate the superiority of the two proposed localization schemes and the proposed CE scheme over state-of-the-art baseline approaches.

Two-Stage Channel Estimation Using Convolutional Neural Networks for IRS-Assisted mmWave Systems

Intelligent reflecting surface (IRS) is expected to be an essential component of next-generation wireless communication networks due to its potential to provide similar or higher array gain with lower hardware cost and energy consumption compared with massive multiple-input--multiple-output (MIMO) technology. However, channel estimation in IRS-assisted communication systems is more challenging than that in conventional systems. In this article, we propose a two-stage channel estimation approach using deep learning in millimeter-wave (mmWave) communication systems with a hybrid passive/active IRS structure. In the first stage, the sparsity of the mmWave massive MIMO channel in the angular domain is exploited to estimate the amplitude of the sparse channel through a convolutional neural network. In this way, the indices of the nonzero entries of the sparse channel can be simultaneously obtained. In the second stage, the channel is reconstructed by solving a least squares problem with the acquired indices. The simulation results show that the proposed channel estimation scheme could achieve better performance with manageable complexity over the existing solutions.

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.

2D Off-Grid Decomposition and SBL Combination for OTFS Channel Estimation

Orthogonal time-frequency space (OTFS) is a promising technique for high mobility wireless communications, as it approaches a sparse representation of doubly-selective fading channel and can obtain delay-Doppler (DD) diversity gain. However, in practical OTFS systems, fractional channel parameter is a great challenge and will seriously deteriorates the sparsity and the performance of channel estimation. In this paper, we propose a novel channel estimation scheme for doubly selective channel in the presence of both fractional Doppler and fractional delay. First, we analyze the input-output relationship of single-input single-output (SISO) OTFS system based on the integer sampling DD grid, then extend it to the fractional DD grid. Compared with the integer DD domain, the model in the fractional DD domain is sparser and more accurate. Based on our analysis, two kinds of off-grid models, i.e., doubly fractional model and mixed one- and two-dimensional (1&2D) fractional models are proposed and compared. The mixed 1&2D fractional model has an advantage of low complexity, but encounters a tandem off-grid distortion. To tackle this distortion problem, as well as to balance the accuracy and the computational workload, a novel two dimensional off-grid decomposition and combination scheme is proposed based on the doubly and mixed 1&2D fractional model. Simulation results demonstrate the effectiveness and superiority of the proposed channel estimation schemes, compared with the existing work for OTFS systems under doubly selective channels.

Sparse Channel Estimation With Surface Clustering for IRS-Assisted OFDM Systems

Intelligent reflecting surface (IRS) is deemed as a potential technology for future communications due to its adaptive enhancement for the propagation environment. To achieve the passive beamforming gain of IRS, accurate channel state information (CSI) is essential but practically challenging since its massive passive reflecting elements have no transmitting/receiving capability. This paper presents a new channel estimation problem formulation for IRS-assisted orthogonal frequency division multiplexing (OFDM) systems, where the channel sparsity is exploited in the time-domain. Considering the surfaces are physically close to each other, we further utilize the common sparsity among the different sub-surfaces and automatically cluster them into several groups by introducing a Dirichlet process (DP)-based clustering model. Then, a DP-based variational Bayesian inference (VBI) framework is proposed to jointly estimate the channel and cluster the sub-surfaces, which is expected to significantly improve the channel estimation performance. Moreover, a novel decoupling trick is combined into the VBI framework to efficiently handle the coupling effect brought by the reflection coefficients, as well as facilitate the Bayesian inference. Simulation results verify the effectiveness of the proposed channel estimation scheme and show its significant performance improvement over various benchmark schemes.

Channel estimation using hybrid optimizer based recurrent neural network long short term memory for MIMO communications in 5G network

In the fifth-generation (5G) networks, multiple input multiple-output (MIMO) systems are further developed to enhance transmission reliability. However, channel estimation is one of the major challenges which needs focus for improved data transmission in MIMO. Although efficient estimation techniques have been recently proposed, estimation accuracy needs to be upgraded further. Hence, an optimized Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM) network is presented in this paper for channel estimation. At first, history of channel responses of pilot block is collected or estimated using Least Square (LS) channel estimation method. Using these collected channel responses, the proposed RNN-LSTM is trained where weight parameters are chosen optimally using hybrid Particle Swarm Optimization (PSO)-Adam optimizer. Using the trained PSO-Adam optimizer based RNN-LSTM, the current channel response is predicted or estimated. The performance of the proposed channel estimation scheme is analysed by varying pilot sequence length and number of antennas to evaluate the metrics Bit Error Rate (BER) and Mean Square Error (MSE). Complexity analysis of the proposed scheme is compared with standard estimators like LS and Minimum Mean-Square Error (MMSE).

UNDERWATER CHANNEL RECOVERY SCHEME IN DELAY-DOPPLER DOMAIN USING MODIFIED BASIC PURSUIT DENOISING WITH PRIOR KNOWLEDGE

The signal denoising problem is a major challenge in underwater communication (UWC). The denoising problem in UWC has been addressed, where channel estimation approaches are provided in terms of sparsity, time, and frequency. Convex optimization is currently widely used for recovering sparse signals from compressed data. A nonuniform sparse 2D frequency-domain channel recovery scheme in the delay-Doppler domain representation is provided as a mathematical framework to solve channel estimation problems. Compressive sensing (CS) at higher Doppler frequencies (high channel mobility) and near full-rate sampling at lower Doppler frequencies (stagnant channel) are used to recover both rapid-fluctuating as well as slow-varying channel components with high precision. The prior knowledge of modified basic-pursuit denoising channel estimation based on nonuniform CS and sparsity-constrained least-squares has been discovered, where <i>l</i><sub>1</sub>-minimization of element wise multiplication of the equal-size vectors is performed. The proposed channel estimation scheme is compared with conventional CS over a sampling ratio, ambient noise level, and observation window length. The acoustic channel simulations and experimental channel data from SPACE08 are used to verify prior knowledge provided by the proposed scheme. Simulation results confirm the superiority of the proposed algorithm for channel estimation over the existing ones.

Parametric Channel Estimation for LoS Dominated Holographic Massive MIMO Systems

Holographic massive multiple-input multiple-output (MIMO), in which a spatially continuous surface is being used for signal transmission and reception, has emerged as a promising solution for improving the coverage and data rate of wireless communication systems. To realize these objectives, the acquisition of accurate channel state information in holographic massive MIMO systems is crucial. This paper proposes a channel estimation scheme based on a parametric physical channel model for line-of-sight (LoS) dominated communication in millimeter and terahertz wave bands. The proposed channel estimation scheme exploits the specific structure of the radiated beams generated by the continuous surface to estimate the channel parameters in a dominated LoS channel model. Since the number of unknown channel parameters is fixed regardless of the number of antennas, the training overhead of the proposed scheme does not scale with the number of antennas. The simulation results demonstrate that the proposed estimation scheme significantly outperforms other benchmark schemes in a poor scattering environment.

Hierarchical BEM Based Channel Estimation With Very Low Pilot Overhead for High Mobility MIMO-OFDM Systems

Channel estimation for multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems is a challenging issue in high mobility scenarios due to prohibitive pilot overhead and complexity. In this paper, a hierarchical basis expansion model (BEM) based channel estimation scheme is proposed for doubly selective channels, which requires a low pilot overhead and computational complexity. A mixed-BEM is proposed for time-varying channels alongside a complex exponential-BEM (CE-BEM), resulting in very few unknown parameters required for channel estimation. Based on the hierarchical BEM structure, an analytical channel estimation model is derived. By employing the block-structured sparsity and a small number of mixed-BEM coefficients in the antenna-time-BEM domain, a low-complexity estimator is proposed to recover the mixed-BEM coefficients accurately. The lower bound on the mean square error (MSE) of channel estimation is derived to verify the effectiveness of the proposed scheme. Simulation results show that the proposed channel estimation scheme significantly outperforms the existing methods in terms of MSE of channel estimation and bit error rate, with much lower pilot overhead and computational complexity.

An embedded pilot power based channel estimation and low-complexity feedback equalization scheme for OTFS system

Orthogonal Time Frequency Space modulation (OTFS) has evolved as an astounding modulation technique for high-speed communication in a doubly dispersive channel. In any wireless communication system, channel estimation and equalization are essential at the receiver to recover the transmitted data. To accomplish this for the emerging OTFS based systems, a modified embedded pilot-based channel estimation technique and low complexity feedback equalization algorithm for integer Doppler shifts in the delay-Doppler domain are proposed in this paper. Our channel estimation scheme exploits embedded-pilot arrangement, and the symbol equalization relies on the Interference calculation and its mitigation iteratively. To achieve this we contemplate a prudent arrangement of symbols in the OTFS frame in such a way that the Guard symbols prevent the interference between data symbols and the pilot symbol at the receiver. Two distinct lumps of received data of the same OTFS frame will be engaged in channel estimation and data detection. An analytical expression of the theoretical Cramer Rao Lower Bound (CRLB) is derived and plotted for the proposed channel estimation scheme. The attained simulation results for Bit-Error-Rate (BER) under the proposed scheme show a significant error rate improvement over the Minimum Mean Squared Error (MMSE) equalization algorithm. Further, a lower computational complexity is also achieved in comparison with modified MMSE detection and MP detection algorithms.

Off-Grid Channel Estimation With Sparse Bayesian Learning for OTFS Systems

This paper proposes an off-grid channel estimation scheme for orthogonal time-frequency space (OTFS) systems adopting the sparse Bayesian learning (SBL) framework. To avoid channel spreading caused by the fractional delay and Doppler shifts and to fully exploit the channel sparsity in the delay-Doppler (DD) domain, we estimate the original DD domain channel response rather than the effective DD domain channel response as commonly adopted in the literature. OTFS channel estimation is firstly formulated as a one-dimensional (1D) off-grid sparse signal recovery (SSR) problem based on a virtual sampling grid defined in the DD space, where the on-grid and off-grid components of the delay and Doppler shifts are separated for estimation. In particular, the on-grid components of the delay and Doppler shifts are jointly determined by the entry indices with significant values in the recovered sparse vector. Then, the corresponding off-grid components are modeled as hyper-parameters in the proposed SBL framework, which can be estimated via the expectation-maximization method. To strike a balance between channel estimation performance and computational complexity, we further propose a two-dimensional (2D) off-grid SSR problem via decoupling the delay and Doppler shift estimations. In our developed 1D and 2D off-grid SBL-based channel estimation algorithms, the hyper-parameters are updated alternatively for computing the conditional posterior distribution of channels, which can be exploited to reconstruct the effective DD domain channel. Compared with the 1D method, the proposed 2D method enjoys a much lower computational complexity while only suffers a slight performance degradation. Simulation results verify the superior performance of the proposed channel estimation schemes over state-of-the-art schemes.

Channel estimation via GAMP for millimeter wave hybrid massive MIMO systems

Abstract In this paper, we study the channel estimation problem for the millimeter wave (mm-wave) hybrid massive multiple-input multiple-output (MIMO) system. Based on the sparsity exhibited by the mm-wave channel in the angular domain, we select a portion of the beams that contribute to the communication process from the beamspace. Then, only the channel coefficients in the selected beamspace need to be estimated. The channel estimation problem is converted into a sparse signal reconstruction problem and solved by employing the generalized approximate message passing (GAMP) algorithm with Laplace distribution. The scale parameter of distribution is designed by analyzing the variance of the partial channel coefficients. Because only the partial channel coefficients need to be estimated, the computational complexity of the proposed GAMP approach is much lower than other methods. Simulation results show that the proposed channel estimation scheme exhibits significant advantages both in terms of estimation accuracy and spectral efficiency.

Semi-Passive Elements Assisted Channel Estimation for Intelligent Reflecting Surface-Aided Communications

In this paper, we propose a novel semi-passive elements-aided channel estimation framework for intelligent reflecting surface (IRS), where a small portion of IRS reflecting elements are able to process the received signal for facilitating the channel estimation. Specifically, the BS-IRS channel is estimated by applying the estimation of signal parameters via rotational invariance technique (ESPRIT), while the user-IRS channels are estimated by combining the use of total least square (TLS) ESPRIT and multiple signal classification (MUSIC) methods. The required training time of the proposed channel estimation scheme is irrelevant to the number of IRS reflecting elements, thus substantially reducing the training overhead. Simulation results show the great advantages of our proposed scheme over both the conventional compressed sensing (CS)-based channel estimation and cascaded channel estimation schemes.

Iteratively reweighted super-resolution channel estimation in hardware-impaired hybrid-precoded MM-wave massive MIMO systems using dual SVD and Marquardt's global search

In this paper, a three-dimensional geometric channel model characterized by channel gain, angle of arrival, and departure (AoA/AoD) is used to develop a non-ideal and more realistic system representation that accounts for additional signal perturbations other than additive white Gaussian noise (AWGN). Hinged on this, compressive sensing (CS) based channel estimation technique is proposed for millimeter-wave (MM-Wave) massive multiple-input multiple-output (MIMO) systems with inherent challenges due to hardware impairment (HI). The proposed estimator named dual singular value decomposition (SVD) and Marquardt, abbreviated as DSM estimator aims at tolerating complexity and improving channel estimation accuracy at reasonable trade-offs in the system. The normalized mean square error (NMSE) performance of the proposed channel estimation scheme, with modified Marquardt's global minimum search, achieves higher accuracy at below 0 dB (LoS)/-3 dB (NLoS) signal-to-noise ratio (SNR) for uniform linear array (ULA) configuration and performs better for uniform planar array (UPA) configuration. A low-rank range of unit difference is adopted for matrix decomposition twice (dual SVD) per SNR in ULA/UPA setting. The proposed estimator's result shows early convergence during the simulation and a linearly scaled complexity in comparison with one of the earlier proposed schemes in literature in particular the super-resolution channel estimation with gradient descent optimization.

Channel Estimation for Intelligent Reflecting Surface Assisted Backscatter Communication

Intelligent reflecting surface (IRS) is a promising technology to improve the performance of backscatter communication systems by smartly reconfiguring the multi-reflection channel. To fully exploit the passive beamforming gain of IRS in backscatter communication, channel state information (CSI) is indispensable but more practically challenging to acquire than conventional IRS-assisted systems, since IRS passively reflects signals over both the forward and backward (backscattering) links between the reader and tag. To address this issue, we propose in this letter a new and efficient channel estimation scheme for the IRS-assisted backscatter communication system. To minimize the mean-square error (MSE) of channel estimation, we formulate and solve an optimization problem by designing the IRS training reflection matrix for channel estimation under the constraints of unit-modulus elements and full rank. Simulation results verify the effectiveness of the proposed channel estimation scheme as compared to other baseline schemes.

Cascaded Channel Estimation for RIS Assisted mmWave MIMO Transmissions

Channel estimation is challenging for the reconfigurable intelligence surface (RIS) assisted millimeter wave (mmWave) communications. Since the number of coefficients of the cascaded channels in such systems is closely dependent on the product of the number of base station antennas and the number of RIS elements, the pilot overhead would be prohibitively high. We propose a cascaded channel estimation framework for an RIS assisted mmWave multiple-input multiple-output system, where the wideband effect on transmission model is considered. Then, we transform the wideband channel estimation into a parameter recovery problem and use a few pilot symbols to detect the channel parameters by the Newtonized orthogonal matching pursuit algorithm. Moreover, the Cramer-Rao lower bound on the channel estimation is introduced. Numerical results show the effectiveness of the proposed channel estimation scheme.

Efficient Channel Estimation for Double-IRS Aided Multi-User MIMO System

To achieve the more significant passive beamforming gain in the double-intelligent reflecting surface (IRS) aided system over the conventional single-IRS counterpart, channel state information (CSI) is indispensable in practice but also more challenging to acquire, due to the presence of not only the single- but also double-reflection links that are intricately coupled and also entail more channel coefficients for estimation. In this paper, we propose a new and efficient channel estimation scheme for the double-IRS aided multi-user multiple-input multiple-output (MIMO) communication system to resolve the cascaded CSI of both its single- and double-reflection links. First, for the single-user case, the single- and double-reflection channels are efficiently estimated at the multi-antenna base station (BS) with both the IRSs turned ON (for maximal signal reflection), by exploiting the fact that their cascaded channel coefficients are scaled versions of their superimposed lower-dimensional CSI. Then, the proposed channel estimation scheme is extended to the multi-user case, where given an arbitrary user's cascaded channel (estimated as in the single-user case), the other users' cascaded channels can also be expressed as lower-dimensional scaled versions of it and thus efficiently estimated at the BS. Simulation results verify the effectiveness of the proposed channel estimation scheme and joint training reflection design for double IRSs, as compared to various benchmark schemes.