Showing papers on "Compressed sensing published in 2021"

Non-Bayesian Activity Detection, Large-Scale Fading Coefficient Estimation, and Unsourced Random Access With a Massive MIMO Receiver

Abstract: In this paper, we study the problem of user activity detection and large-scale fading coefficient estimation in a random access wireless uplink with a massive MIMO base station with a large number $M$ of antennas and a large number of wireless single-antenna devices (users). We consider a block fading channel model where the $M$ -dimensional channel vector of each user remains constant over a coherence block containing $L$ signal dimensions in time-frequency. In the considered setting, the number of potential users $K_{\text {tot}}$ is much larger than $L$ but at each time slot only $K_{a} \ll K_{\text {tot}}$ of them are active. Previous results, based on compressed sensing, require that $K_{a}\le L $ , which is a bottleneck in massive deployment scenarios. In this work, we show that such limitation can be overcome when the number of base station antennas $M$ is sufficiently large. More specifically, we prove that with a coherence block of dimension $L$ and a number of antennas $M$ such that $K_{a}/M = o(1)$ , one can identify $K_{a} = O\left({L^{2}/\log ^{2}\left({\frac {K_{\text {tot}}}{K_{a}}}\right)}\right)$ active users, which is much larger than the previously known bounds. We also provide two algorithms. One is based on Non-Negative Least-Squares, for which the above scaling result can be rigorously proved. The other consists of a low-complexity iterative componentwise minimization of the likelihood function of the underlying problem. While for this algorithm a rigorous proof cannot be given, we analyze a constrained version of the Maximum Likelihood (ML) problem (a combinatorial optimization with exponential complexity) and find the same fundamental scaling law for the number of identifiable users. Therefore, we conjecture that the low-complexity (approximated) ML algorithm also achieves the same scaling law and we demonstrate its performance by simulation. We also compare the discussed methods with the (Bayesian) MMV-AMP algorithm, recently proposed for the same setting, and show superior performance and better numerical stability. Finally, we use the discussed approximated ML algorithm as the inner decoder in a concatenated coding scheme for unsourced random access , a grant-free uncoordinated multiple access scheme where all users make use of the same codebook, and the receiver must produce the list of transmitted messages, irrespectively of the identity of the transmitters. We show that reliable communication is possible at any $E_{b}/N_{0}$ provided that a sufficiently large number of base station antennas is used, and that a sum spectral efficiency in the order of $\mathcal {O}(L\log (L))$ is achievable.

Image encryption based on compressed sensing and DNA encoding

Abstract: An image encryption scheme based on a four-wing hyperchaotic system combined with compressed sensing and DNA coding is proposed The scheme uses compressed sensing (CS) to reduce the image according to a certain scale in the encryption process The measurement matrix is constructed by combining the Kronecker product (KP) and chaotic system KP is used to extend the low-dimensional seed matrix to the high-dimensional measurement matrix The dimensional seed matrix is generated by a four-wing chaotic system At the same time, the chaotic sequence generated by the chaotic system dynamically controls the DNA coding and then performs the XOR operation Simulation experiments and performance analysis show that the algorithm has good performance and security

AMP-Net: Denoising-Based Deep Unfolding for Compressive Image Sensing

Abstract: Most compressive sensing (CS) reconstruction methods can be divided into two categories, i.e. model-based methods and classical deep network methods. By unfolding the iterative optimization algorithm for model-based methods onto networks, deep unfolding methods have the good interpretation of model-based methods and the high speed of classical deep network methods. In this article, to solve the visual image CS problem, we propose a deep unfolding model dubbed AMP-Net. Rather than learning regularization terms, it is established by unfolding the iterative denoising process of the well-known approximate message passing algorithm. Furthermore, AMP-Net integrates deblocking modules in order to eliminate the blocking artifacts that usually appear in CS of visual images. In addition, the sampling matrix is jointly trained with other network parameters to enhance the reconstruction performance. Experimental results show that the proposed AMP-Net has better reconstruction accuracy than other state-of-the-art methods with high reconstruction speed and a small number of network parameters.

Detecting Compressed Deepfake Videos in Social Networks Using Frame-Temporality Two-Stream Convolutional Network

Abstract: The development of technologies that can generate Deepfake videos is expanding rapidly. These videos are easily synthesized without leaving obvious traces of manipulation. Though forensically detection in high-definition video datasets has achieved remarkable results, the forensics of compressed videos is worth further exploring. In fact, compressed videos are common in social networks, such as videos from Instagram, Wechat, and Tiktok. Therefore, how to identify compressed Deepfake videos becomes a fundamental issue. In this paper, we propose a two-stream method by analyzing the frame-level and temporality-level of compressed Deepfake videos. Since the video compression brings lots of redundant information to frames, the proposed frame-level stream gradually prunes the network to prevent the model from fitting the compression noise. Aiming at the problem that the temporal consistency in Deepfake videos might be ignored, we apply a temporality-level stream to extract temporal correlation features. When combined with scores from the two streams, our proposed method performs better than the state-of-the-art methods in compressed Deepfake videos detection.

A High-Quality Photon-Counting CT Technique Based on Weight Adaptive Total-Variation and Image-Spectral Tensor Factorization for Small Animals Imaging

Abstract: Photon-counting X-ray computed tomography (CT) has been attracting great attention in tissue characterization, material discrimination, and so on. The emitting X-ray energy spectrum cutting into several energy bins that can result in only a part of X-ray photons can be collected within each narrow bin. This can compromise the image quality. In this case, how to obtain high-quality tomography is a big challenge. In this study, to overcome these issues, we mainly focus on developing an advanced imaging software based on the latest photon-counting CT system (MARS scanner). Specifically, we first design a weight adaptive total variation (TV) using compressed sensing theory. Then, combining the weight adaptive TV and nonlocal low-rank tensor factorization to formulate a new weight adaptive total-variation and image-spectral tensor factorization (WATITF) model for high-quality imaging. Finally, the optimization model is performed to obtain its solution. The studies including the numerical and preclinical mice are performed to validate and evaluate its outperformance.

A Compressive Sensing Approach for Federated Learning Over Massive MIMO Communication Systems

Abstract: Federated learning is a privacy-preserving approach to train a global model at a central server by collaborating with wireless devices, each with its own local training data set. In this paper, we present a compressive sensing approach for federated learning over massive multiple-input multiple-output communication systems in which the central server equipped with a massive antenna array communicates with the wireless devices. One major challenge in system design is to reconstruct local gradient vectors accurately at the central server, which are computed-and-sent from the wireless devices. To overcome this challenge, we first establish a transmission strategy to construct sparse transmitted signals from the local gradient vectors at the devices. We then propose a compressive sensing algorithm enabling the server to iteratively find the linear minimum-mean-square-error (LMMSE) estimate of the transmitted signal by exploiting its sparsity. We also derive an analytical threshold for the residual error at each iteration, to design the stopping criterion of the proposed algorithm. We show that for a sparse transmitted signal, the proposed algorithm requires less computationally complexity than LMMSE. Simulation results demonstrate that the presented approach outperforms conventional linear beamforming approaches and reduces the performance gap between federated learning and centralized learning with perfect reconstruction.

Deep SAR Imaging and Motion Compensation

Abstract: Compressive sensing (CS) and matrix sensing (MS) techniques have been applied to the synthetic aperture radar (SAR) imaging problem to reduce the sampling amount of SAR echo using the sparse or low-rank prior information. To further exploit the redundancy and improve sampling efficiency, we take a different approach, wherein a deep SAR imaging algorithm is proposed. The main idea is to exploit the redundancy of the backscattering coefficient using an auto-encoder structure, wherein the hidden latent layer in auto-encoder has lower dimension and less parameters than the backscattering coefficient layer. Based on the auto-encoder model, the parameters of the auto-encoder structure and the backscattering coefficient are estimated simultaneously by optimizing the reconstruction loss associated with the down-sampled SAR echo. In addition, in order to meet the practical application requirements, a deep SAR motion compensation algorithm is proposed to eliminate the effect of motion errors on imaging results. The effectiveness of the proposed algorithms is verified on both simulated and real SAR data.

COAST: COntrollable Arbitrary-Sampling NeTwork for Compressive Sensing

Abstract: Recent deep network-based compressive sensing (CS) methods have achieved great success. However, most of them regard different sampling matrices as different independent tasks and need to train a specific model for each target sampling matrix. Such practices give rise to inefficiency in computing and suffer from poor generalization ability. In this paper, we propose a novel COntrollable Arbitrary-Sampling neTwork, dubbed COAST, to solve CS problems of arbitrary-sampling matrices (including unseen sampling matrices) with one single model. Under the optimization-inspired deep unfolding framework, our COAST exhibits good interpretability. In COAST, a random projection augmentation (RPA) strategy is proposed to promote the training diversity in the sampling space to enable arbitrary sampling, and a controllable proximal mapping module (CPMM) and a plug-and-play deblocking (PnP-D) strategy are further developed to dynamically modulate the network features and effectively eliminate the blocking artifacts, respectively. Extensive experiments on widely used benchmark datasets demonstrate that our proposed COAST is not only able to handle arbitrary sampling matrices with one single model but also to achieve state-of-the-art performance with fast speed. The source code is available on this https URL.

Image Restoration via Reconciliation of Group Sparsity and Low-Rank Models

Abstract: Image nonlocal self-similarity (NSS) property has been widely exploited via various sparsity models such as joint sparsity (JS) and group sparse coding (GSC). However, the existing NSS-based sparsity models are either too restrictive, e.g. , JS enforces the sparse codes to share the same support, or too general, e.g. , GSC imposes only plain sparsity on the group coefficients, which limit their effectiveness for modeling real images. In this paper, we propose a novel NSS-based sparsity model, namely, low-rank regularized group sparse coding (LR-GSC) , to bridge the gap between the popular GSC and JS. The proposed LR-GSC model simultaneously exploits the sparsity and low-rankness of the dictionary-domain coefficients for each group of similar patches. An alternating minimization with an adaptive adjusted parameter strategy is developed to solve the proposed optimization problem for different image restoration tasks, including image denoising, image deblocking, image inpainting, and image compressive sensing. Extensive experimental results demonstrate that the proposed LR-GSC algorithm outperforms many popular or state-of-the-art methods in terms of objective and perceptual metrics.

Deep Learning Approach for Sparse Aperture ISAR Imaging and Autofocusing Based on Complex-Valued ADMM-Net

Abstract: Sparse aperture radar imaging is generally achieved by methods of compressive sensing (CS), or, sparse signal recovery(SSR). However, most of the traditional SSR methods cannot produce focused image stably, which limits their applications. ${l}_{1}$ regularization and alternating direction method of multipliers(ADMM) are generally applied to the SSR problem, but its performance is sensitive to the selection of model parameters. This paper proposes a complex-valued ADMM-Net(CV-ADMMN) method to improve the stability of ADMM, and utilize it to achieve sparse aperture ISAR imaging and autofocusing. Firstly, the iterative procedure of ADMM is unrolled to be a deep network structure. Then, the parameters of the model are learned from a training dataset by utilizing an ${l}_{1}$ regularized loss function. Finally, an autofocusing module based on entropy-minimization is plugged into the trained model to compensate the phase error. Experimental results based on both simulated and measured data validate the superiority of the proposed method over ADMM.

Multi-Channel Deep Networks for Block-Based Image Compressive Sensing

Abstract: Incorporating deep neural networks in image compressive sensing (CS) receives intensive attentions in multimedia technology and applications recently. As deep network approaches learn the inverse mapping directly from the CS measurements, the reconstruction speed is significantly faster than the conventional CS algorithms. However, for existing network-based approaches, a CS sampling procedure has to map a separate network model. This may potentially degrade the performance of image CS with block-wise sampling because of blocking artifacts, especially when multiple sampling rates are assigned to different blocks within an image. In this paper, we develop a multi-channel deep network for block-based image CS by exploiting inter-block correlation with performance significantly exceeding the current state-of-the-art methods. The significant performance improvement is attributed to block-wise approximation but full-image removal of blocking artifacts. Specifically, with our multi-channel structure, the image blocks with a variety of sampling rates can be reconstructed in a single model. The initially reconstructed blocks are then capable of being reassembled into a full image to improve the recovered images by unrolling a hand-designed block-based CS recovery algorithm. Experimental results demonstrate that the proposed method outperforms the state-of-the-art CS methods by a large margin in terms of objective metrics and subjective visual image quality. Our source codes are available at https://github.com/siwangzhou/DeepBCS .

Hybrid Evolutionary-based Sparse Channel Estimation for IRS-assisted mmWave MIMO Systems

Abstract: 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.

Compressed Sensing Framework for Heart Sound Acquisition in Internet of Medical Things

Abstract: For continuous monitoring of cardiovascular diseases, this paper presents a novel framework for heart sound acquisition. The proposed approach uses compressed sensing for signal sampling, and a two-stage reconstruction is developed for reconstruction. The first stage aims to give a tentative recovered signal, on which a peak detection technique is developed to identify whether there is a peak in current segment and, if so, its location. With such information, an adaptive dictionary is selected for the second round reconstruction. Because the selected dictionary is adaptive to the morphology of current frame, the signal reconstruction performance is consequently promoted. Experiment results indicate that a satisfactory performance can be obtained when the frame length is 256 and the signal morphology is divided into 16 categories. Furthermore, the proposed algorithm is compared with a series of counterparts and the results well demonstrate the advantages of our proposal, especially at high compression ratios.

Approximate Message Passing With Unitary Transformation for Robust Bilinear Recovery

Abstract: Recently, several promising approximate message passing (AMP) based algorithms have been developed for bilinear recovery with model $\boldsymbol{Y}=\sum _{\boldsymbol{k}=1}^{\boldsymbol{K}} \boldsymbol{b}_{\boldsymbol{k}} \boldsymbol{A}_{\boldsymbol{k}} \boldsymbol{C}+\boldsymbol{W}$ , where $\lbrace \boldsymbol{b}_{\boldsymbol{k}}\rbrace$ and $\boldsymbol{C}$ are jointly recovered with known $\boldsymbol{A}_k$ from the noisy measurements $\boldsymbol{Y}$ . The bilinear recovery problem has many applications such as dictionary learning, self-calibration, compressive sensing with matrix uncertainty, etc. In this work, we propose a new approximate Bayesian inference algorithm for bilinear recovery, where AMP with unitary transformation (UTAMP) is integrated with belief propagation (BP), variational inference (VI) and expectation propagation (EP) to achieve efficient approximate inference. It is shown that, compared to state-of-the-art bilinear recovery algorithms, the proposed algorithm is much more robust and faster, leading to remarkably better performance.

AF-AMPNet: A Deep Learning Approach for Sparse Aperture ISAR Imaging and Autofocusing

Abstract: Inverse synthetic aperture radar (ISAR) imaging and autofocusing are challenging under sparse aperture (SA) conditions. Traditional imaging or autofocusing methods fail to obtain satisfying results due to the nonuniform and incomplete data caused by SA. To address this problem, a novel compressive sensing (CS)-based imaging and autofocusing framework is proposed to obtain high cross-range resolution for SA ISAR. To achieve well-focused imaging results of better performance and higher efficiency simultaneously, we merge the phase error estimation into the CS framework, then iteratively solve the compound CS problem in matrix form with approximate message-passing (AMP), dubbed as AF-AMP. Moreover, a deep learning approach is also proposed by mapping AF-AMP into a deep network, dubbed as AF-AMPNet, with extensive modifications to further improve the efficiency. The adaptively and layer-wisely optimal parameters learned by the training process are also promising to enhance the performance and robustness against noise. Besides, the loss function for training is subjoined with regularized l₁ and l₂ constraints to ensure the sparsity and quality of imaging results. Furthermore, the proposed AF-AMP and corresponding network-based AF-AMPNet are verified by simulated and measured experiments, both of which show superior performance, robustness, and higher efficiency than other state-of-the-art methods. AF-AMPNet can achieve the best performance in much less computational time.

Jointly Sparse Signal Recovery and Support Recovery via Deep Learning With Applications in MIMO-Based Grant-Free Random Access

Abstract: In this article, we investigate jointly sparse signal recovery and jointly sparse support recovery in Multiple Measurement Vector (MMV) models for complex signals, which arise in many applications in communications and signal processing. Recent key applications include channel estimation and device activity detection in MIMO-based grant-free random access which is proposed to support massive machine-type communications (mMTC) for Internet of Things (IoT). Utilizing techniques in compressive sensing, optimization and deep learning, we propose two model-driven approaches, based on the standard auto-encoder structure for real numbers. One is to jointly design the common measurement matrix and jointly sparse signal recovery method, and the other aims to jointly design the common measurement matrix and jointly sparse support recovery method. The proposed model-driven approaches can effectively utilize features of sparsity patterns in designing common measurement matrices and adjusting model-driven decoders, and can greatly benefit from the underlying state-of-the-art recovery methods with theoretical guarantee. Hence, the obtained common measurement matrices and recovery methods can significantly outperform the underlying advanced recovery methods. We conduct extensive numerical results on channel estimation and device activity detection in MIMO-based grant-free random access. The numerical results show that the proposed approaches provide pilot sequences and channel estimation or device activity detection methods which can achieve higher estimation or detection accuracy with shorter computation time than existing ones. Furthermore, the numerical results explain how such gains are achieved via the proposed approaches.

Robust Coding of Encrypted Images via 2D Compressed Sensing

Abstract: In many practical scenarios, image encryption should be implemented before image compression. This leads to the requirement of compressing encrypted images. Compressed sensing (CS), a breakthrough in signal processing, has been demonstrated to be an effective method for compressing encrypted images with robustness. However, for the exiting CS-based image encryption-then-compression (ETC) systems, image encryption is usually performed by using linear operations. When linear operations are used, we cannot achieve low computational complexity and high security in the meantime. To solve this problem, a novel 2D CS (2DCS) based ETC (2DCS-ETC) scheme is proposed in this paper. First, two nonlinear operations, including global random permutation (GRP) and negative-positive transformation (NPT), are utilized to encrypt the original image for high security purpose. Second, the encrypted image is compressed by using 2DCS for low computational complexity purpose. Furthermore, a gray mapping operation is embedded prior to CS encoding. Since gray mapping strategy can reduce the dynamic range of the CS samples, this strategy is also helpful for the rate distortion (R-D) performance improvement. Third, a 2D projected gradient with embedding decryption (2DPG-ED) algorithm is proposed, which can be utilized for the original image reconstruction even if the encrypted image is not sparse anymore. Compared with the previous CS-based ETC methods, the proposed approach can simultaneously achieve high security and low computational complexity with better robustness.

COAST: COntrollable Arbitrary-Sampling NeTwork for Compressive Sensing

Abstract: Recent deep network-based compressive sensing (CS) methods have achieved great success. However, most of them regard different sampling matrices as different independent tasks and need to train a specific model for each target sampling matrix. Such practices give rise to inefficiency in computing and suffer from poor generalization ability. In this paper, we propose a novel COntrollable Arbitrary-Sampling neTwork, dubbed COAST, to solve CS problems of arbitrary-sampling matrices (including unseen sampling matrices) with one single model. Under the optimization-inspired deep unfolding framework, our COAST exhibits good interpretability. In COAST, a random projection augmentation (RPA) strategy is proposed to promote the training diversity in the sampling space to enable arbitrary sampling, and a controllable proximal mapping module (CPMM) and a plug-and-play deblocking (PnP-D) strategy are further developed to dynamically modulate the network features and effectively eliminate the blocking artifacts, respectively. Extensive experiments on widely used benchmark datasets demonstrate that our proposed COAST is not only able to handle arbitrary sampling matrices with one single model but also to achieve state-of-the-art performance with fast speed.

Video SAR Imaging Based on Low-Rank Tensor Recovery

Abstract: Due to its ability of forming continuous images for a ground scene of interest, the video synthetic aperture radar (SAR) has been studied in recent years. However, as video SAR needs to reconstruct many frames, the data are of enormous amount and the imaging process is of large computational cost, which limits its applications. In this article, we exploit the redundancy property of multiframe video SAR data, which can be modeled as low-rank tensor, and formulate the video SAR imaging process as a low-rank tensor recovery problem, which is solved by an efficient alternating minimization method. We empirically compare the proposed method with several state-of-the-art video SAR imaging algorithms, including the fast back-projection (FBP) method and the compressed sensing (CS)-based method. Experiments on both simulated and real data show that the proposed low-rank tensor-based method requires significantly less amount of data samples while achieving similar or better imaging performance.

Chaos and compressive sensing based novel image encryption scheme

Abstract: Out of various cryptographic attacks, Chosen-Plaintext Attack (CPA) is one of the most powerful and widely used attack on encrypted images. In order to efficiently resist such a strong attack, a novel chaos and compressive sensing based image encryption algorithm is presented in this work. Firstly, the original plaintext image is compressed via Orthogonal Matching Pursuit with Partially Known Support (OMP-PKS) and then the compressed image is confused and diffused using TD-ERCS and Skew-tent chaotic maps, respectively. Correlation among the compressed pixels is break down via confusing the image pixels using Tangent Delay Ellipse Reflecting Cavity Map System (TD-ERCS). Skew-tent chaotic map is employed for the pixel diffusion process. To get the final ciphertext image, the confused pixels are further changed through bitwise XORed operation via random matrix. For the sake of higher security, the initial conditions of chaotic maps are made dependent on the plaintext image and the parameters are computed via SHA-512. Furthermore, to decrease the transmission bandwidth, the measurement matrix is generated via Beta chaotic map. Instead of sending the whole measurement matrix, the sender will just send the Beta chaotic map initial conditions and control parameters (key) values along with the compressed ciphertext. The reliability and robustness of the designed image compression and encryption scheme are verified via experimental analysis and simulation results. All the experimental and simulation results are in favor of the proposed scheme.

An adjustable visual image cryptosystem based on 6D hyperchaotic system and compressive sensing

Abstract: In this paper, an efficient and adjustable visual image encryption scheme is proposed by combining a 6D hyperchaotic system, compressive sensing, and Bezier curve embedding. First, the plain image is sparse by discrete wavelet transform (DWT). Then, the sparse image is encrypted and compressed through game-of-life (GOL) hybrid scrambling and compressive sensing into a cipher image. Next, Bezier curve embedding is utilized to embed the cipher image into the carrier image in wavelet domain. After these operations, the final visually meaningful steganographic image is generated. Additionally, the frequency-domain information of the plain image is used to generate the initial values of the 6D hyperchaotic system in scrambling process, which makes the proposed encryption scheme able to effectively resist the chosen-plaintext attacks (CPA) and the known-plaintext attacks (KPA). Moreover, our scheme exhibits excellent adjustable performance compared with existing related schemes. Ultimately, simulation results and comprehensive performance analyses demonstrate that the scheme proposed in this paper has high decryption quality, visual security, robustness, and operating efficiency.

Compressive sensing chaotic encryption algorithms for OFDM-PON data transmission

Abstract: In this paper, we propose chaotic compressive sensing (CS) encryption algorithms for orthogonal frequency division multiplexing passive optical network (OFDM-PON), aiming at compressing the transmitted data and enhancing the security of data transmission. Bitstream transmission using CS directly is restricted due to its inability to satisfy the sparsity in neither time nor frequency domain. While the sparsity of the transmitted data can be constructed when transmitting the multimedia. A sensor can be then used to identify whether the data is multimedia. If it is, the CS technique is used, and the sensor’s result is set as side information inserted into the pilot and transmitted to the terminal simultaneously. For encryption processing, a 2-dimensional logistic-sine-coupling map (2D-LSCM) is used to generate pseudo-random numbers to construct the first row of a measurement matrix to encrypt the system. Four transform formats are then applied to generate the sparsity of the transmitted data. Due to the restriction of data transmission in the physical layer, the discrete cosine transform (DCT) is chosen to conduct the CS technique. Four approximation algorithms are also proposed to optimize the performance of compressing the length of bits. We find that ‘Round + Set negative to 0’ shows the best performance. The combination of this chaotic CS encryption technique with the OFDM-PON systems saves the bandwidth and improves the security.

Deep-Learned Approximate Message Passing for Asynchronous Massive Connectivity

Abstract: This paper considers the massive connectivity problem in an asynchronous grant-free random access system, where a huge number of devices sporadically transmit data to a base station (BS) with imperfect synchronization The goal is to design algorithms for joint user activity detection, delay detection, and channel estimation By exploiting the sparsity on both user activity and delays, we formulate a hierarchical sparse signal recovery problem in both the single-antenna and the multiple-antenna scenarios While traditional compressed sensing algorithms can be applied to these problems, they suffer high computational complexity and often require the perfect statistical information of channel and devices This paper solves these problems by designing the Learned Approximate Message Passing (LAMP) network, which belongs to model-driven deep learning approaches and ensures efficient performance without tremendous training data Particularly, in the multiple-antenna scenario, we design three different LAMP structures, namely, distributed, centralized and hybrid ones, to balance the performance and complexity Simulation results demonstrate that the proposed LAMP networks can significantly outperform the conventional AMP method thanks to their ability of parameter learning It is also shown that LAMP has robust performance to the maximal delay spread of the asynchronous users

TPSSI-Net: Fast and Enhanced Two-Path Iterative Network for 3D SAR Sparse Imaging

Abstract: The emerging field of combining compressed sensing (CS) and three-dimensional synthetic aperture radar (3D SAR) imaging has shown significant potential to reduce sampling rate and improve image quality. However, the conventional CS-driven algorithms are always limited by huge computational costs and non-trivial tuning of parameters. In this article, to address this problem, we propose a two-path iterative framework dubbed TPSSI-Net for 3D SAR sparse imaging. By mapping the AMP into a layer-fixed deep neural network, each layer of TPSSI-Net consists of four modules in cascade corresponding to four steps of the AMP optimization. Differently, the Onsager terms in TPSSI-Net are modified to be differentiable and scaled by learnable coefficients. Rather than manually choosing a sparsifying basis, a two-path convolutional neural network (CNN) is developed and embedded in TPSSI-Net for nonlinear sparse representation in the complex-valued domain. All parameters are layer-varied and optimized by end-to-end training based on a channel-wise loss function, bounding both symmetry constraint and measurement fidelity. Finally, extensive SAR imaging experiments, including simulations and real-measured tests, demonstrate the effectiveness and high efficiency of the proposed TPSSI-Net.

RMIST-Net: Joint Range Migration and Sparse Reconstruction Network for 3-D mmW Imaging

Abstract: Compressed sensing (CS) demonstrates significant potential to improve image quality in 3-D millimeter-wave imaging compared with conventional matched filtering (MF). However, existing sparsity-driven 3-D imaging algorithms always suffer from large-scale storage, excessive computational cost, and nontrivial tuning of parameters due to the huge-dimensional matrix-vector multiplication in complicated iterative optimization steps. In this article, we present a novel range migration (RM) kernel-based iterative-shrinkage thresholding network, dubbed as RMIST-Net, by combining the traditional model-based CS method and data-driven deep learning method for near-field 3-D millimeter-wave (mmW) sparse imaging. First, the measurement matrices in ISTA optimization steps are replaced by RM kernels, by which matrix-vector multiplication is converted to the Hadamard product. Then, the modified ISTA optimization is unrolled into a deep hierarchical architecture, in which all parameters are learned automatically instead of manually tuned. Subsequently, 1000 pairs of oracle images with randomly distributed targets and their corresponding echoes are simulated to train the network. A well-trained RMIST-Net produces high-quality 3-D images from range-focused echoes. Finally, we experimentally prove that RMIST-Net is capable process 512 x 512 large-scale imaging tasks within 1 s. Besides, we compare RMIST-Net with other state-of-the-art methods in near-field 3-D imaging applications. Both simulations and real-measured experiments demonstrate that RMIST-Net produces impressive reconstruction performance while maintaining high computational speed compared with conventional and sparse imaging algorithms.

SAR Parametric Super-Resolution Image Reconstruction Methods Based on ADMM and Deep Neural Network

Abstract: The compressed sensing (CS)-based synthetic aperture radar (SAR) imaging methods have emerged as the standard approach to obtain super-resolution (SR) SAR images and achieve extraordinary performances However, they face three challenges First, this kind of method is mainly based on the point scattering model and not suitable for characterizing the line-segment-scattering and surface-scattering features of distributed targets Second, the hyperparameters in these methods are hard to tune to optimal values Third, due to a large amount of calculation, these methods are difficult to apply in practice In this article, to solve these problems, we introduce the line-segment-scatterers (LSSs) and rectangular-plate-scatterers (RPSs) in SAR echo model to develop the SAR hybrid echo model and propose two SAR parametric SR image reconstruction methods based on solving a CS problem, where three penalties are utilized to exploit the sparsity of the point scatterers, LSSs, and RPSs, respectively At the core of the first method is a direct solver called multicomponent alternating direction method of multipliers (MC-ADMM) solver that solves the CS problem quickly and iteratively based on closed derivative expressions In contrast, the second method maps the MC-ADMM solver into a deep unfolded neural network, ie, the parametric SR imaging network (PSRI-Net), which is faster, and the parameters can be automatically set to the optimum Since all the parameters of the MC-ADMM solver are learned discriminatively through end-to-end training in PSRI-Net Extensive simulation and practical experiments are carried out to demonstrate the effectiveness of the proposed methods

l ₀- l ₁ Hybrid Total Variation Regularization and its Applications on Hyperspectral Image Mixed Noise Removal and Compressed Sensing

Abstract: The total variation (TV) regularization has been widely used in various applications related to hyperspectral (HS) signal and image processing due to its potential in modeling the underlying smoothness of HS data. However, most existing TV norms usually tend to generate spatial oversmoothing or artifacts. To this end, we propose a novel $l_{0}$ - $l_{1}$ hybrid TV ( $l_{0}$ - $l_{1}$ HTV) regularization with the applications to HS mixed noise removal and compressed sensing (CS). More specifically, $l_{0}$ - $l_{1}$ HTV can be regarded as a globally and locally integrated TV regularizer, where the $l_{0}$ gradient constraint is incorporate into the $l_{1}$ spatial–spectral TV ( $l_{1}$ -SSTV). $l_{1}$ -SSTV is capable of exploiting the local structure information across both spatial and spectral domains, while the $l_{0}$ gradient can promote a globally spectral–spatial smoothness by directly controlling the number of nonzero gradients of HS images. This efficient combination considers more comprehensive prior knowledge of HS images, yielding sharper edge preservation and resolving the above drawbacks of existing pure TV norms. More significantly, $l_{0}$ - $l_{1}$ HTV can be easily injected into HS-related processing models, and an effective algorithm based on the alternating direction method of multipliers (ADMM) is developed to solve the optimization problems. Extensive experiments conducted on several HS data sets substantiate the superiority and effectiveness of the proposed method in comparison with many state-of-the-art methods.