Sparse Channel Research Articles

Multi-scenario millimeter wave wireless channel measurements and sparsity analysis

Wireless channel characteristics have significant impacts on channel modeling, estimation, and communication performance. While the channel sparsity is an important characteristic of wireless channels. Utilizing the sparse nature of wireless channels can reduce the complexity of channel modeling and estimation, and improve system design and performance analysis. Compared with the traditional sub-6 GHz channel, millimeter wave (mmWave) channel has been considered to be more sparse in existing researches. However, most research only assume that the mmWave channel is sparse, without providing quantitative analysis and evaluation. Therefore, this paper evaluates the sparsity of mmWave channels based on mmWave channel measurements. A vector network analyzer (VNA)-based mmWave channel sounder is developed to measure the channel at 28 GHz, and multi-scenario channel measurements are conducted. The Gini index, Rician K factor and root-mean-square (RMS) delay spread are used to measure channel sparsity. Then, the key factors affecting mmWave channel sparsity are explored. It is found that antenna steering direction and scattering environment will affect the sparsity of mmWave channel. In addition, the impact of channel sparsity on channel eigenvalue and capacity is evaluated and analyzed.

UAV Jitter May Have Merit: A Fading Analysis in Air-to-Sea Two Ray Channels

Jitter effects caused by airflow and vibrations could be harmful to UAV communications, e.g., it may cause mismatch between the transmit beam and actual user direction. However, jitter introduces additional randomness to the channel, which may have merit in sparse channels that are relatively static and deep faded. For this consideration, we pick the commonly used air-to-sea two ray propagation model and investigate the impact of UAV jitter on the channel fading statistics. Assuming a Gaussian random model for the jitter angle, we derive analytical expressions for the jitter-beneficial probability (the probability that UAV jitter increases the receive power), and the average receive power under jitter, respectively. Accordingly, the jitter-beneficial conditions are analyzed and verified via simulations.

Low-Complexity MMSE Receiver Design for Massive MIMO OTFS Systems

We design a low-complexity minimum mean square error (MMSE) receiver for a practical rectangular pulse-shaped orthogonal time frequency space (OTFS) massive multiple-input multiple-output (mMIMO) system. The proposed receiver is designed by exploiting the OTFS channel sparsity, and the structure of matrices involved in the MMSE receiver. We show that the sparse OTFS channel matrix is multi-banded, and has a high bandwidth. This is because its non-zero elements, which occur in multiple bands, are scattered throughout the matrix. We reduce its bandwidth by using the Reverse Cuthill-Mckee algorithm. The proposed MMSE receiver does not use any approximation, and thus has the same bit error rate (BER) as that of its conventional counterpart. We show that the proposed receiver, without any BER loss, achieves significantly lower complexity than the existing state-of-the-art receivers.

Modeling and channel estimation for piezo-acoustic backscatter assisted underwater acoustic communications

Relying on direct and converse piezoelectric effects, piezo-acoustic backscatter (PAB) technology reflects ambient acoustic signals to enable underwater backscatter communications at near-zero power, which was first realized through a prototype. In this paper, we propose a mathematical model of the PAB assisted underwater acoustic (UWA) communication, and address the sparse channel estimation problem. First, we present a five-stage backscatter process to derive the backscatter coefficient, and propose the channel model for the shallow-water communications. Then, we formulate the shallow-water acoustic channel estimation problem as a sparse vector recovery one according to the compressed sensing theory, and leverage the orthogonal matching pursuit (OMP) algorithm to obtain the channel estimator. Finally, simulation results are provided to corroborate our proposed studies.

Sparse Channel Pruning and Assistant Distillation for Faster Aerial Object Detection

In recent years, object detectors based on convolutional neural networks have been widely used on remote sensing images. However, the improvement of their detection performance depends on a deeper convolution layer and a complex convolution structure, resulting in a significant increase in the storage space and computational complexity. Although previous works have designed a variety of new lightweight convolution and compression algorithms, these works often require complex manual design and cause the detector to be greatly modified, which makes it difficult to directly apply the algorithms to different detectors and general hardware. Therefore, this paper proposes an iterative pruning framework based on assistant distillation. Specifically, a structured sparse pruning strategy for detectors is proposed. By taking the channel scaling factor as a representation of the weight importance, the channels of the network are pruned and the detector is greatly slimmed. Then, a teacher assistant distillation model is proposed to recover the network performance after compression. The intermediate models retained in the pruning process are used as assistant models. By way of the teachers distilling the assistants and the assistants distilling the students, the students’ underfitting caused by the difference in capacity between teachers and students is eliminated, thus effectively restoring the network performance. By using this compression framework, we can greatly compress the network without changing the network structure and can obtain the support of any hardware platform and deep learning library. Extensive experiments show that compared with existing detection networks, our method can achieve an effective balance between speed and accuracy on three commonly used remote sensing target datasets (i.e., NWPU VHR-10, RSOD, and DOTA).

Robust uplink channel estimation for massive MIMO systems with general array geometry

Least-squares (LS) estimator is the most commonly used uplink channel estimator for massive multiple-input multiple-output (MIMO) systems, but its performance depends critically on the choice of the training pilots and is also sensitive to outlier measurements. In this paper, we exploit the channel sparsity to significantly improve the uplink channel estimation performance for any pilot pattern in the presence of impulsive noise. The key to the success of the proposed method is the independent variational Bayesian inference (VBI) factorization imposed in the sparse Bayesian learning (SBL) framework, which can efficiently separate the sparse signal-of-interest and the sparse representation of the impulsive noise, as well as decoupling sparse signals from the pilots automatically. Moreover, we demonstrate that the proposed method can be extended to a general array geometry by introducing a coarse non-uniform sampling 2D-grid into the SBL framework. Simulation results verify the superiority of the developed method.

Model-Driven Deep Learning Assisted Beam Tracking for Millimeter-Wave Systems

Millimeter-wave (mmWave) systems employ directional beams to provide high-speed data communication, which demands continuous tracking of the beam direction. Currently, the technology based on Kalman filter and/or channel sparsity is considered for beam tracking, and its solutions are based on specific assumptions, which may not apply to the actual scene. In this letter, a model-driven deep learning (MD-DL) network is proposed, which combines the traditional signal processing method with the convolutional neural network for beam tracking. Specifically, the traditional signal processing is designed to drive the network for enhancing the process of feature extraction. In contrast to previous work based on deep learning, due to model-driven, fewer pilot sequences are required to track the arrival angle of the main path. The simulation results demonstrate that the proposed MD-DL network can improve the beam tracking accuracy and reduce the probability of losing tracking when vehicles travel at 72 km/h in complex urban scenarios.

Joint Channel Estimation and Data Detection for Hybrid RIS Aided Millimeter Wave OTFS Systems

For high mobility communication scenario, the recently emerged orthogonal time frequency space (OTFS) modulation introduces a new delay-Doppler domain signal space, and can provide better communication performance than traditional orthogonal frequency division multiplexing system. This article focuses on the joint channel estimation and data detection (JCEDD) for hybrid reconfigurable intelligent surface (HRIS) aided millimeter wave (mmWave) OTFS systems. Firstly, a new transmission structure is designed. Within the pilot durations of the designed structure, partial HRIS elements are alternatively activated. The time domain channel model is then exhibited. Secondly, the received signal model for both the HRIS over time domain and the base station over delay-Doppler domain are studied. Thirdly, by utilizing channel parameters acquired at the HRIS, an HRIS beamforming design strategy is proposed. For the OTFS transmission, we propose a JCEDD scheme over delay-Doppler domain. In this scheme, message passing (MP) algorithm is designed to simultaneously obtain the equivalent channel gain and the data symbols. On the other hand, the channel parameters, i.e., the Doppler shift, the channel sparsity, and the channel variance, are updated through expectation-maximization (EM) algorithm. By iteratively executing the MP and EM algorithm, both the channel and the unknown data symbols can be accurately acquired. Finally, simulation results are provided to validate the effectiveness of our proposed JCEDD scheme.

Dictionary Learning-Based Channel Estimation for RIS-Aided MISO Communications

In this letter, we study the channel estimation problem for the reconfigurable intelligent surface (RIS)-aided multi-input single-output (MISO) system. By exploiting the channel sparsity, compressive sensing (CS) based sparse channel estimators can be applied to the system to reduce the training overhead. However, these existing sparse channel estimators adopt predefined dictionaries when formulating the sparse matrix recovery problem, which will cause grid mismatch issues and estimation performance degradation. Hence, in this letter, we formulate the channel estimation problem as a joint dictionary parameter learning and sparse signal recovery problem, in which the dictionary parameter can be optimized to adapt to the channel measurements, thereby improving the robustness of sparse channel representation and estimation performance. Then, we propose an iterative re-weighted algorithm to solve this non-convex problem efficiently. Simulation results show that the proposed algorithm outperforms other benchmark schemes significantly.

Random Access With Massive MIMO-OTFS in LEO Satellite Communications

This paper considers the joint channel estimation and device activity detection in the grant-free random access systems, where a large number of Internet-of-Things devices intend to communicate with a low-earth orbit satellite in a sporadic way. In addition, the massive multiple-input multiple-output (MIMO) with orthogonal time-frequency space (OTFS) modulation is adopted to combat the dynamics of the terrestrial-satellite link. We first analyze the input-output relationship of the single-input single-output OTFS when the large delay and Doppler shift both exist, and then extend it to the grant-free random access with massive MIMO-OTFS. Next, by exploring the sparsity of channel in the delay-Doppler-angle domain, a two-dimensional pattern coupled hierarchical prior with the sparse Bayesian learning and covariance-free method (TDSBL-FM) is developed for the channel estimation. Then, the active devices are detected by computing the energy of the estimated channel. Finally, the generalized approximate message passing algorithm combined with the sparse Bayesian learning and two-dimensional convolution (ConvSBL-GAMP) is proposed to decrease the computations of the TDSBL-FM algorithm. Simulation results demonstrate that the proposed algorithms outperform conventional methods.

Deep Unfolding Basis Pursuit: Improving Sparse Channel Reconstruction via Data-Driven Measurement Matrices

For massive multiple-input multiple-output systems operating in frequency-division duplex mode, downlink channel state information (CSI) acquisition will incur large overhead. This overhead is substantially reduced when sparse channel estimation techniques are employed, owing to the channel sparsity in the angular domain. When a sparse channel estimation method is implemented, the measurement matrix, which is related to the pilot matrix, is essential to the channel estimation performance. Existing sparse channel estimation schemes widely adopt random measurement matrices, which have been criticized for their suboptimal reconstruction performance. This paper proposes novel data-driven solutions to design the measurement matrix. Model-based autoencoders are customized to optimize the measurement matrix by unfolding the classical basis pursuit algorithm. The obtained data-driven measurement matrices are applied to existing sparse reconstruction algorithms, leading to flexible hybrid data-driven implementations for sparse channel estimation. Numerical results show that the proposed data-driven measurement matrices can achieve more accurate reconstructions and use fewer measurements than the existing random matrices, thereby leading to a higher achievable rate for CSI acquisition. Moreover, compared with existing pure deep learning-based sparse reconstruction methods, the proposed hybrid data-driven scheme, which uses the novel data-driven measurement matrices with conventional sparse reconstruction algorithms, can achieve higher reconstruction accuracy.

Training Beam Design for Channel Estimation in Hybrid mmWave MIMO Systems

Training beam design for channel estimation with infinite-resolution and low-resolution phase shifters (PSs) in hybrid analog-digital milimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems is considered in this paper. By exploiting the sparsity of mmWave channels, the optimization of the sensing matrices (corresponding to training beams) is formulated according to the compressive sensing (CS) theory. Under the condition of infinite-resolution PSs, we propose relevant algorithms to construct the sensing matrix, where the theory of convex optimization and the gradient descent in Riemannian manifold is used to design the digital and analog part, respectively. Furthermore, a block-wise alternating hybrid analog-digital algorithm is proposed to tackle the design of training beams with low-resolution PSs, where the performance degeneration caused by non-convex constant modulus and discrete phase constraints is effectively compensated to some extent thanks to the iterations among blocks. Finally, the orthogonal matching pursuit (OMP) based estimator is adopted for achieving an effective recovery of the sparse mmWave channel. Simulation results demonstrate the performance advantages of proposed algorithms compared with some existing schemes.

Hybrid Beamforming for Intelligent Reflecting Surface Aided Millimeter Wave MIMO Systems

As communication systems that employ millimeter wave (mmWave) frequency bands must use large antenna arrays to overcome the severe propagation loss of mmWave signals, hybrid beamforming has been considered as an integral component of mmWave communications. Recently, intelligent reflecting surface (IRS) has been proposed as an innovative technology that can significantly improve the performance of mmWave communication systems through the use of low-cost passive reflecting elements. In this paper, we study IRS-aided mmWave multiple-input multiple-output (MIMO) systems with hybrid beamforming architectures. We first exploit the sparse-scattering structure and large dimension of mmWave channels to develop the joint design of IRS reflection matrix and hybrid beamformer for narrowband MIMO systems. Then, we generalize the proposed joint design to broadband MIMO systems with orthogonal frequency division multiplexing (OFDM) modulation by leveraging the angular sparsity of frequency-selective mmWave channels. Simulation results demonstrate that the proposed joint designs can significantly enhance the spectral efficiency of the systems of interest and achieve superior performance over the existing designs.

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 With Reconfigurable Intelligent Surfaces—A General Framework

Optimally extracting the advantages available from reconfigurable intelligent surfaces (RISs) in wireless communications systems requires estimation of the channels to and from the RIS. The process of determining these channels is complicated when the RIS is composed of passive elements without any sensing or data processing capabilities, and thus, the channels must be estimated indirectly by a noncolocated device, typically a controlling base station (BS). In this article, we examine channel estimation for passive RIS-based systems from a fundamental viewpoint. We study various possible channel models and the identifiability of the models as a function of the available pilot data and behavior of the RIS during training. In particular, we will consider situations with and without line-of-sight propagation, single-antenna and multi-antenna configurations for the users and BS, correlated and sparse channel models, single-carrier and wideband orthogonal frequency-division multiplexing (OFDM) scenarios, availability of direct links between the users and BS, exploitation of prior information, as well as a number of other special cases. We further conduct simulations of representative algorithms and comparisons of their performance for various channel models using the relevant Cramér-Rao bounds.

A New Network Pruning Framework Based on Rewind

Model pruning is one of the main methods of deep neural network model compression. However, the existing model pruning methods are inefficient, there is still a lot of redundancy in the network, and the pruning has a great impact on the accuracy. In addition, the traditional pruning process usually needs to fine-tune the network to restore accuracy, and fine-tuning has a limited effect on accuracy recovery, so it is difficult to achieve a high level of accuracy. In this paper, we propose a new neural network pruning framework: the channel sparsity is realized by introducing a scale factor, and the sparse network is pruned by setting a global threshold, which greatly improves the efficiency of pruning. After pruning, this paper proposes a rewind method to restore the accuracy, that is, save the weight after training, and then reload it on the network for training to restore the accuracy. In addition, we also study the best rewind point of the three networks. The experimental results show that our method significantly reduces the number of parameters and FLOPs without affecting or even improving the accuracy, and the rewind method proposed by us achieves a better accuracy recovery effect than fine-tuning. At the same time, we find that the epoch with the highest accuracy is the best rewind point, and the accuracy is the highest after saving its corresponding weight and retraining the model.

A mixing regularization parameter IPNLMS for underwater acoustic MIMO channel estimation

Less sensitivity to the sparsity of the underwater acoustic (UWA) channel is a drawback of the improved proportionate normalized least-mean-square (IPNLMS) algorithm. To address this problem, we propose an IPNLMS algorithm with a mixing regularization parameter (MRP-IPNLMS) for the multiple-input multiple-output (MIMO) UWA channel estimation. Firstly, the proposed MRP-IPNLMS exploits the sparsity of the UWA channel and integrates an approximate L0 norm into the cost function of the IPNLMS method, achieving the fast convergence in the sparse UWA channel estimation. Furthermore, we adopt a time-varying mixing regularization parameter to dynamically adjust the cost function. The proposed MRP-IPNLMS algorithm adaptively combines the fast convergence of the small regularization parameter with the low steady-state misalignment of the large regularization parameter. In addition, we evaluate the performance of the proposed algorithm in terms of its convergence, steady-state misalignment, and output signal-to-noise ratio (OSNR). Finally, simulations and real-data experiments are also conducted to demonstrate the effectiveness of the proposed algorithm.

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.

Sparse Bayesian Learning Based Channel Extrapolation for RIS Assisted MIMO-OFDM

Reconfigurable intelligent surface (RIS) is a revolutionary technology and can be used to assist communication systems by adaptively manipulating the wireless environment. In this paper, we propose a novel cascaded channel estimation algorithm for the RIS-assisted multiple-input multiple-output orthogonal frequency division multiplexing systems. Inspired by the channel compression idea, we can obtain a sub-sampled channel by turning off a fraction of RIS elements and then extrapolate it to the complete one, by which the pilot overhead is greatly reduced. The problem of channel extrapolation is transformed into recovering the physical parameters of the cascaded channel from the partial channel observations and is then formulated by the sparse Bayesian learning (SBL) framework. In order to circumvent the curse of high dimensional matrices inversion in the vector-matrix system, we further introduce the tensor structure into the SBL framework. Specially, by leveraging of the channel sparsity over the angle domain and delay domain, we derive the virtual channel expression in tensor form and model a Kronecker-structured prior distribution for the virtual channels. The multi-domain sparse properties of the virtual channel tensor can be effectively captured by a group of low-dimensional hyper-parameters, and thus reduce the computational complexity. In addition, the Cramer-Rao lower bound is derived for the proposed channel extrapolation. Simulation results show the superior performance of the proposed scheme.

Uplink Channel Estimation With Reduced Fronthaul Overhead in Cell-Free Massive MIMO Systems

This letter focuses on the problem of uplink channel estimation with the reduced fronthaul overhead in cell-free massive multiple-input multiple-output (mMIMO) systems. First, we propose a sub-sampling scheme to reduce the dimension of fronthaul. Then, we exploit the inherent channel sparsity and model the underdetermined channel estimation problem as an off-grid sparse signal recovery problem. Finally, an enhanced sparse Bayesian learning (ESBL) channel estimation algorithm is proposed to refine the sampled grid points and recover the sparse channel iteratively. Simulation results demonstrate that the proposed algorithm achieves a significant reduction on the fronthaul overhead and offers a better channel estimation performance.