Compressive Channel Estimation Research Articles

Off-grid compressed channel estimation with parallel interference cancellation for millimeter wave massive MIMO

Off-grid compressed channel estimation with parallel interference cancellation for millimeter wave massive MIMO

A Low-Complexity Channel Estimation in Internet of Vehicles in Intelligent Transportation Systems for 5G Communication

The objective of utilizing mmWave/subTHz bands in next-generation wireless communications is to be achieved. Despite this, since reconfigurable intelligent surface (RIS)-assisted systems depend on the transmission channel configuration, the system architecture design, and the methods used to derive channel state information (CSI) on a base station (BS) and RIS, channel estimation continues to be the main problem with these systems. This research proposes an innovative RIS-based and compressed sensing-based channel estimation technique for the internet of vehicles. To obtain the best phase shift matrix, the communication model must first be constructed, and the angle-of-arrival and departure are utilized. Channel estimation is then performed based on the perception matrix. The training overhead and complexity of the channel estimation are reduced by considering the position information of the vehicles in the optimal phase shift matrix. Simulation results show that the proposed algorithm exhibits better channel estimation and low complexity performance compared with existing algorithms.

Bivariate Pilot Optimization for Compressed Channel Estimation in RIS-Assisted Multiuser MISO-OFDM Systems

In reconfigurable intelligent surface (RIS)-assisted wireless communication systems, channel estimation is of paramount importance to unleash the full advantage of the high passive beamforming gains from RIS. However, it is particularly challenging due to the massive number of RIS reflecting elements which lack signal perception and processing capabilities, thus incurring prohibitively high pilot overhead between transceivers. To address this challenge, we focus on an RIS-assisted multiuser multiple-input single-output orthogonal frequency division multiplexing (MU-MISO-OFDM) system, and propose a practical transmission protocol with non-uniformly spaced comb -type pilots for compressed channel estimation and data transmission, by exploiting the sparsity of RIS-associated channels. To solve a bivariate pilot optimization problem with the objective of minimizing the mutual coherence of the measurement matrix influenced by pilot positions and pilot powers simultaneously, we propose two enhanced whale optimization algorithm (EWOA)-based approaches by leveraging the second order cone programming (SOCP) and compressed coding scheme (CCS). Simulation results validate the effectiveness of the proposed methods over benchmark schemes in various aspects. It corroborates that considering the coupling relationship between pilot positions and powers can help improve the channel estimation performance of the RIS-assisted MU-MISO-OFDM system with limited pilot overhead.

Unsourced Random Access Over Frequency-Selective Channels

We propose and study unsourced random access over frequency-selective channels via orthogonal frequency division multiplexing, employed to mitigate the channel effects. The decoder utilizes a compressed sensing-based joint activity detection and channel estimation algorithm coupled with an algorithm treating interference as noise and successive interference cancellation. Our results demonstrate that the proposed method offers a competitive performance with grant-based frequency division multiple access; and that, the performance loss with estimated channel state information is less than 2 dB when the number of active users is not very large.

Compressed Channel Estimation for Near-Field XL-MIMO Using Triple Parametric Decomposition

With the presence of the conventional angular-domain representation, the energy leakage effect is evident for the near-field extremely large-scale multiple-input-multiple-output (XL-MIMO) channel, which further leads to ambiguous channel power detection, significantly eroding channel estimation performance. To tackle this challenge, this paper proposes a novel Triple Parametric Decomposition (TPD) framework to facilitate compressed channel estimation in the XL-MIMO system. Specifically, we examine the case of a uniform planar array (UPA) with the geometry parameters involving a pair of azimuth-elevation angles and distances in an attempt to achieve independent decomposition by constructing their own covariance matrices. Then, by utilizing the on- and off-grid techniques, we obtain the sparse recovery of angles and distances information in the absence of energy leakage effect at reduced gridding complexity. Simulation results demonstrate that our proposed TPD achieves significant performance improvement over the state-of-the-art methods.

Channel estimation for reconfigurable intelligent surface-assisted mmWave based on Re‘nyi entropy function

This study focuses on channel estimation for reconfigurable intelligent surface (RIS)-assisted mmWave systems, in which the RIS is used to facilitate base-to-user data transfer. For beamforming to work with active and passive elements, a large-size cascade channel matrix should always be known. Low training costs are achieved by using the mmWave channels’ inherent sparsity. The research provides a unique compressive sensing-based channel estimation approach for reducing pilot overhead issues to a minimum. The proposed technique estimates channel data signals in a downlink for RIS-assisted mmWave systems. The mmWave systems often have a sparse distribution of signal sources due to the spatial correlations of the domains. This distribution pattern makes it possible to use compressive sensing methods to resolve the channel estimation issue. In order to decrease the pilot overhead, which is necessary to predict the channel, the proposed method extends the Re‘nyi entropy function as the sparsity-promoting regularizer. In contrast to conventional compressive sensing techniques, which necessitate an initial knowledge of the signal’s sparsity level, the presented method employs sparsity adaptive matching pursuit (SAMP) techniques to gradually determine the signal’s sparsity level. Furthermore, it introduces a threshold parameter based on the signal’s energy level to eliminate the sparsity level requirement. Extensive simulations show that the presented channel estimation approach surpasses the traditional OMP-based channel estimation methods in terms of normalized mean square error performance. In addition, the computational cost of channel estimation is lowered. Based on the simulations, our approach can estimate the channel well while reducing training overhead by a large amount.

Compressive sensing-based channel estimation for high mobile systems with delay Doppler effects

In this paper, channel overhead is reduced by exploiting channel sparsity for multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) system. Where, compressive sensing (CS) based dictionary design algorithms has been adopted as a channel estimation technique in high mobile systems with minimal number of pilots, such as high-speed train (HST) systems. A novel framework design of the dictionary-based CS was proposed considering both delay and Doppler effects in order to correctly recover the channel response. The channel under consideration is a 2 by 2 space-time block code (STBC) MIMO channel. Simulation tests according to the international telecommunication union (ITU) channel model demonstrated the suitability of the proposed dictionary for estimating the channel impulse response (CIR) of a liner time varying (LTV) channel with a mobility approaches 675 Km/h related to a Doppler frequency of 1500 Hz and 2.4 GHz carrier frequency. Two CS recovery algorithms were applied; orthogonal matching pursuit (OMP) and basis pursuit (BP), where by about 7 dB gain in signal to noise ratio (SNR) was achieved with mobility of 675 Km/h using OMP as compared to BP at a bit error rate (BER) of with 128 OFDM subcarriers.

Compressive sensing-based channel estimation schemes for wideband millimeter wave wireless communication systems

The introduction of both hybrid precoder-combiner structure and low-bit resolution analog-to-digital converters (ADCs) makes channel estimation in millimeter (mm) Wave Massive MIMO systems to be challenging. In the literature, several channel estimation schemes presented focus on narrowband channels. However, few works are found to be presented for the case of wideband channels for this system. In this paper, two new channel estimation techniques are proposed with the first scheme based on the Projection-based Orthogonal Matching Pursuit (POMP) algorithm. The second estimation scheme is based on the Predictive Orthogonal Least Squares (POLS) algorithm. In all the simulation results, POLS shows the best performance but with the highest computational complexity cost, with POMB exhibiting the next best performance with a lower computational complexity cost. In terms of compromise between performance and computational complexity cost, the proposed POLS-based channel estimator is the best estimator for the wideband mmWave Massive MIMO systems.

Compressive Channel Estimation Based on the Deep Denoising Network in an IRS-Enhanced Massive MIMO System.

Integrating large intelligent reflecting surfaces (IRS) into a millimeter-wave (mmWave) massive multi-input-multi-output (MIMO) technique has been a promising approach to enhance the performance of the wireless communication system with the channel state information (CSI). Most existing work assume that ideal channel estimation can be obtained, but the proposed high-dimensional cascaded MIMO channels and passive reflectors pose a great challenge to these methods. To address the abovementioned problems, we proposed a new method for the reduction of training overhead in IRS with a partial ON/OFF model and an optimizing strategy for pilot design approach. The energy consumption of large-scale antenna arrays and the pilot overhead in the training phase of signal transmission are greatly reduced. Besides, we proposed an improved deep residual shrinkage denoising network, which possesses better denoising performance with a soft thresholding model. The channel data can be denoised by deep learning methods, which greatly improve the accuracy of channel estimation. Simulation results demonstrate that the superiority of the proposed network over prior solutions.

Low-Complexity Compressive Channel Estimation for IRS-Aided mmWave Systems With Hypernetwork-Assisted LAMP Network

Intelligent reflecting surface (IRS) can enhance the wireless communication environment by smartly reflecting the incident signal toward desired directions. However, the acquisition of channel state information (CSI) is challenging since IRS usually consists of a massive number of passive elements that have no capabilities of sensing and processing the pilot signals. Although by exploiting the sparsity of the angular domain channel, the huge pilot overhead can be reduced with conventional compressive sensing algorithms, such as approximate message passing (AMP), these algorithms cannot achieve satisfactory channel estimation performance. Based on the learned AMP (LAMP) network, we propose a hypernetwork-assisted LAMP (HN-LAMP) network with dynamic shrinkage parameters to improve the channel estimation accuracy. Furthermore, a recurrent architecture is adopted to reduce the large memory overhead arising from the LAMP network. Simulation results show that the proposed HN-LAMP network can improve the channel estimation accuracy or reduce the computational complexity under satisfactory estimation performance. Moreover, the proposed hypernetwork-assisted recurrent LAMP (HNR-LAMP) architecture can effectively reduce 50% memory overhead by sharing learnable weights.

Deep Compressed Sensing-Based Cascaded Channel Estimation for RIS-Aided Communication Systems

To reduce the pilot overhead of cascaded channel estimation for RIS-aided Massive MIMO communication system, we proposed a deep compressed sensing-based channel estimation scheme, where U-shaped network (U-Net), an encoder-decoder with skip connection, is used to recover the high-dimensional cascaded channel matrix from limited pilot overhead. The skip connections between encoder and decoder can fuse features of different scales and semantic by concatenating the feature map, which enhance the reconstruction performance of cascaded channel. To further improve the feature extraction ability of U-Net, we design a ResU-Net architecture with stacked residual units to increase the depth of network. Simulation results show the channel estimation of ResU-Net is more accurate than conventional algorithm and other network model. Meanwhile, ResU-Net has good generalization and robustness for different pilot lengths and phase quantization errors.

Hybrid Evolutionary-Based Sparse Channel Estimation for IRS-Assisted mmWave MIMO Systems

The intelligent reflecting surface (IRS)-assisted millimeter wave (mmWave) communication system has emerged as a promising technology for coverage extension and capacity enhancement. Prior works on IRS have mostly assumed perfect channel state information (CSI), which facilitates in deriving the upper-bound performance but is difficult to realize in practice due to passive elements of IRS without signal processing capabilities. In this paper, we propose a compressive channel estimation techniques for IRS-assisted mmWave multi-input and multi-output (MIMO) system. To reduce the training overhead, the inherent sparsity of mmWave channels is exploited. By utilizing the properties of Kronecker products, IRS-assisted mmWave channel is converted into a sparse signal recovery problem, which involves two competing cost function terms (measurement error and sparsity term). Existing sparse recovery algorithms solve the combined contradictory objectives function using a regularization parameter, which leads to a suboptimal solution. To address this concern, a hybrid multiobjective evolutionary paradigm is developed to solve the sparse recovery problem, which can overcome the difficulty in the choice of regularization parameter value. Simulation results show that under a wide range of simulation settings, the proposed method achieves competitive error performance compared to existing channel estimation methods.

Joint Synchronization and Compressive Channel Estimation for Frequency-Selective Hybrid mmWave MIMO Systems

Synchronization is a fundamental procedure in cellular systems whereby user equipment (UE) acquires the time and frequency information required to decode the data transmitted by a base station (BS). Due to the small antenna aperture at millimeter wave (mmWave), large antenna arrays are used to compensate for the low signal-to-noise ratio (SNR) at the receiver. For this reason, synchronization is often performed jointly with beam training as in 5G New Radio (NR) to boost the receive SNR. However, if the mmWave multiple-input multiple-output (MIMO) channel is to be estimated, then synchronization must be performed in the low SNR regime owing to the lack of directional beamforming during compressive channel estimation. Current mmWave channel estimation algorithms implicitly take synchronization for granted, which is a not practical assumption in the low SNR regime. To solve this problem, this work proposes the first synchronization framework for hybrid mmWave MIMO that is robust to carrier frequency offset (CFO) and phase noise (PHN) synchronization errors. We propose two novel multi-stage algorithms to jointly estimate the various unknown parameters, based on the expectation-maximization (EM) algorithm. By using the QuaDRiGa 5G NR channel simulator, we show that compressive channel estimation without prior synchronization is possible, and the proposed approaches outperform current solutions for joint beam training and synchronization currently considered in 5G NR.

Channel Estimation for Broadband Millimeter Wave MIMO Systems Based on High-Order PARALIND Model

Channel state information (CSI) is important to improve the performance of wireless transmission. However, the problems of high propagation path loss, multipath, and frequency selective fading make it difficult to obtain the CSI in broadband millimeter-wave (mmWave) system. Based on the inherent multidimensional structure of mmWave multipath channels and the correlation between channel dimensions, mmWave multiple input multiple output (MIMO) channels are modelled as high-order parallel profiles with linear dependence (PARALIND) model in this paper, and a new PARALIND-based channel estimation algorithm is proposed for broadband mmWave system. Due to the structural property of PARALIND model, the proposed algorithm firstly separates the multipath channels of different scatterers by PARALIND decomposition and then estimates the channel parameters from the factor matrices decomposed from the model based on their structures. Meanwhile, the performance of mmWave channel estimation is analysed theoretically. A necessary condition for channel parameter estimation is given based on the uniqueness principle of PARALIND model. Simulation results show that the proposed algorithm performs better than traditional compressive sensing-based channel estimation algorithms.

Site-specific millimeter-wave compressive channel estimation algorithms with hybrid MIMO architectures

In this paper, we present and compare three novel model-cum-data-driven channel estimation procedures in a millimeter-wave Multi-Input Multi-Output (MIMO) Orthogonal Frequency Division Multiplexing (OFDM) wireless communication system. The transceivers employ a hybrid analog-digital architecture. We adapt techniques from a wide range of signal processing methods, such as detection and estimation theories, compressed sensing, and Bayesian inference, to learn the unknown virtual beamspace domain dictionary, as well as the delay-and-beamspace sparse channel. We train the model-based algorithms with a site-specific training dataset generated using a realistic ray tracing-based wireless channel simulation tool. We assess the performance of the proposed channel estimation algorithms with the same site's test data. We benchmark the performance of our novel procedures in terms of normalized mean squared error against an existing fast greedy method and empirically show that model-based approaches combined with data-driven customization unanimously outperform the state-of-the-art techniques by a large margin. The proposed algorithms were selected as the top three solutions in the "ML5G-PHY Channel Estimation Global Challenge 2020" organized by the International Telecommunication Union.

Joint Channel and AoA Estimation in OFDM Systems: One Channel Tap With Multiple AoAs Problem

Recently, a joint channel and angle-of-arrival (AoA) estimation scheme for pilot-assisted multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems with digital arrays has been proposed; in that scheme, compressive sensing-based time-domain channel estimation is applied. In the scheme, it is assumed that each channel tap has only one AoA. However, in reality, one channel tap may contain responses from multiple paths with different AoAs. In this letter, we propose an improved approach to solve the aforementioned problem. The underlying concept is to transmit multiple OFDM symbols with different beamforming vectors, rendering the time-invariant channel equivalently time-variant. Subsequently, the correlation matrix, imbedded with AoA information, can be estimated. Finally, subspace-based algorithms, such as multiple signal classification (MUSIC), can be used for AoA estimation. The advantage of the proposed method is that the number of detectable AoAs is much larger than that in conventional MUSIC. The performance of the proposed method was also theoretically analyzed. Simulation results demonstrated that with a small number of OFDM symbols, both channel gains and AoAs could be satisfactorily estimated and the theoretical analysis results were accurate.

Downlink Compressive Channel Estimation With Phase Noise in Massive MIMO Systems

Phase noise (PN) introduced by the oscillator at the base station and user side severely degrades the channel estimation performance. This paper investigates the impact of PN on downlink compressive channel estimation in massive multiple-input multiple-output (MIMO) systems. Particularly, the downlink compressive channel estimation with PN is modeled as a sparse signal recovery problem with additive correlated perturbation on the pilot matrix, which is a general formulation for both non-synchronous and synchronous PN. Based on this signal model, the performance of the equivalent sensing matrix is analyzed by invoking restricted isometry property (RIP) in compressive sensing. In addition, the upper bound for $l_{1}$ -minimization based channel estimation method and tight channel estimation bound are derived in the framework of RIP and Oracle least square methodology, respectively. Finally, we propose a PN-aware sparse Bayesian learning (PNA-SBL) algorithm to improve the channel estimation performance in the presence of synchronous PN. Simulation results demonstrate our analysis and superiority of the proposed PNA-SBL algorithm.

Deep Denoising Neural Network Assisted Compressive Channel Estimation for mmWave Intelligent Reflecting Surfaces

Integrating large intelligent reflecting surfaces (IRS) into millimeter-wave (mmWave) massive multi-input-multi-ouput (MIMO) has been a promising approach for improved coverage and throughput. Most existing work assumes the ideal channel estimation, which can be challenging due to the high-dimensional cascaded MIMO channels and passive reflecting elements. Therefore, this paper proposes a deep denoising neural network assisted compressive channel estimation for mmWave IRS systems to reduce the training overhead. Specifically, we first introduce a hybrid passive/active IRS architecture, where very few receive chains are employed to estimate the uplink user-to-IRS channels. At the channel training stage, only a small proportion of elements will be successively activated to sound the partial channels. Moreover, the complete channel matrix can be reconstructed from the limited measurements based on compressive sensing, whereby the common sparsity of angular domain mmWave MIMO channels among different subcarriers is leveraged for improved accuracy. Besides, a complex-valued denoising convolution neural network (CV-DnCNN) is further proposed for enhanced performance. Simulation results demonstrate the superiority of the proposed solution over state-of-the-art solutions.

Full spark of even discrete cosine transforms

In this paper, we investigate the spark of a family of transforms that are frequently used in signal processing applications such as Multicarrier Communication systems: we consider the even Discrete Cosine Transform matrices (DCTs). On one hand, we provide several theorems that prove that the submatrices obtained from its first rows present maximum spark; on the other hand, we give counterexamples of other sets of rows that do not present this good property. Besides the mathematical interest of our contributions, we also show that our results have important novel applications in compressed sensing problems such as sparse signal reconstruction and compressed channel estimation. We also demonstrate that a channel filter can be perfectly estimated by means of a small amount of its DCT coefficients, which can be furthermore arbitrarily selected. Thus, random compressive sampling schemes are valid for solving channel estimation problems when using even DCTs. Finally, numerical simulations show the good performance of the DCT transforms for practical sparse signal recovery in both noisy and noise-free scenarios.

Joint Nuclear Norm and ℓ1–2-Regularization Sparse Channel Estimation for mmWave Massive MIMO Systems

Millimeter-wave massive MIMO can effectively improve the signal-to-noise ratio, but the high-dimensional channel matrix significantly increases the complexity of the classic channel estimation algorithm. On the other hand, millimeter-wave massive MIMO has low rank and sparsity properties in the angle domain. Combining these two properties can effectively improve the channel estimation accuracy. This article proposes a novel millimeter-wave sparse channel estimation method based on joint nuclear norm and $\ell _{1-2}$ -regularization. The basic idea of the proposed algorithm is to formulate the channel estimation problem as a compressed sensing problem. This method constructs an objective function consisted of $\ell _{1-2} $ -regularization, and the resulting nuclear norm minimization problems is optimized via the alternating direction method of multipliers (ADMM) algorithm. The simulation results verified that the proposed method can provide better estimation accuracy compared with the state-of-the-art compressed sensing-based channel estimation methods.