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We propose a compressed sensing algorithm termed variance state propagation (VSP) for block-sparse signals, i.e., sparse signals that have nonzero coefficients occurring in clusters. The VSP algorithm is developed under the Bayesian framework. A hierarchical Gaussian prior is introduced to depict the clustered patterns in the sparse signal. Markov random field (MRF) is introduced to characterize the state of the variances of the Gaussian priors. Such a hierarchical prior has the potential to encourage clustered patterns and suppress isolated coefficients whose patterns are different from their respective neighbors. The core idea of our algorithm is to iteratively update the variances in the prior Gaussian distribution. The message passing technique is employed in the design of the algorithm. For messages that are difficult to calculate, we correspondingly design reasonable methods to achieve approximate calculations. The hyperparameters can be updated within the iteration process. Simulation results demonstrate that the VSP algorithm is able to handle a variety of block-sparse signal recovery tasks and presents a significant advantage over the existing methods.

International audience; Reconfigurable intelligent surface (RIS), one of the key enablers for the sixth-generation (6G) mobile communication networks, is considered by designers to smartly reconfigure the wireless propagation environment in a controllable and programmable manner. Specifically, an RIS consists of a large number of low-cost and passive reflective elements (REs) without radio frequency chains. The system gain of RIS wireless systems can be achieved by adjusting the phase shifts and amplitudes of the REs so that the desired signals can be added constructively at the receiver. However, an RIS typically has limited signal processing capability and cannot perform active transmitting/receiving in general, which leads to new challenges in the physical layer design of RIS wireless systems. In this paper, we provide an overview of the RIS-aided wireless systems, including the reflection principle, channel estimation, and system design. In particular, two types of emerging RIS systems are considered: RIS-aided wireless communications (RAWC) and RIS-based information transmission (RBIT), where the RIS plays the role of the reflector and the transmitter, respectively. We also envision the potential applications of RIS in 6G networks.

This paper studies semi-blind channel estimation for a reconfigurable intelligent surface (RIS) aided uplink massive multiple-input multiple-output (MIMO) system, in which the base station simultaneously estimates the channel coefficients and detects the partially unknown transmit symbols. We formulate the semi-blind channel estimation task as a trilinear inference problem. Based on the approximate message passing (AMP) principle, we develop a computationally efficient approach, called Trilinear AMP, to calculate the marginal posterior mean estimators of the trilinear inference problem. Simulation results demonstrate the effectiveness of the proposed Trilinear AMP approach.

This correspondence paper provides a novel super-resolution downlink channel estimation approach for massive multiple-input multiple-output (MIMO) systems, by jointly learning the parametric dictionary and recovering the sparse channel components. Specifically, we exploit a Markov spike and slab prior to characterize the clustered sparse channel structure resulting from small local scatterers in the angular domain. The proposed algorithm is developed within a variational expectation maximization framework and integrated with the generalized approximate message passing technique to calculate the intractable posterior distribution. Simulation results illustrate that our approach attains a significant performance improvement over existing methods.

This paper investigates the passive beamforming and information transfer (PBIT) technique for the large intelligent surface (LIS) aided multiuser multiple-input multiple-output (MuMIMO) systems, where the LIS is deployed to enhance the primary communication via passive beamforming and simultaneously deliver its private data via spatial modulation. For the passive beamforming design, we propose to maximize the sum channel capacity of the LIS-aided Mu-MIMO systems and formulate the maximization problem as a two-step stochastic program. An efficient sample average approximation based iterative algorithm is developed for the passive beamforming design. For the receiver design, the signal detection at the receiver is a bilinear estimation problem since the LIS data are multiplicatively modulated onto the reflected signals of the reflecting elements. To solve this problem, we develop a turbo message passing algorithm in which the bilinear estimation problem is divided into two subproblems: one for the estimation of the user signals and the other for the estimation of the LIS data. Extensive simulation results are provided to demonstrate the advantages of our passive beamforming and receiver designs.

This letter is concerned with the symmetric nonnegative matrix factorization in the presence of heavy-tailed outliers. We address this problem under a formulation involving some robust loss functions, instead of the standard squared-error loss. To handle the original computationally intractable problem, we present an efficient block iteratively reweighted algorithmic framework with provable convergence guarantee. Each iteration of the proposed method is obtained by minimizing a fourth-order nonnegative polynomial optimization with a closed-form solution. Simulation results illustrate that the proposed algorithm attains a significant performance improvement over existing benchmark methods in heavy-tailed noise environment.

This letter concentrates on the spectrum sensing problem for secondary user equipped with a large number of low-correlated antennas, and a robust blind detection method based on Ljung-Box (LB) test is proposed, which leverages the autocorrelation function of the received time series to evaluate the signal existence. In the scenarios where the correlation between receive antennas is low, simulation results illustrate that the proposed LB-test method attains a significant detection performance improvement compared with the existing covariance-based methods in the time-varying Rayleigh fading channel.

This letter concentrates on the problem of spectral compressed sensing in impulsive noise, which aims to recover a spectrally sparse signal from its contaminated and undersampled measurements. We propose a robust formulation for joint sparse signal and frequency recovery, which includes the generalized $\ell _p$ -norm $(0 data-fidelity fitting term added to a log-sum sparsity-promoting regularizer. To handle this intractable issue, we develop an iteratively reweighted $\ell _2$ approach via majorizing the original objective function by a quadratic surrogate function. Simulation results illustrate that the proposed approach attains a significant performance improvement over the existing methods under impulsive noise.

This paper investigates the passive beamforming and information transfer (PBIT) technique for the multiuser multiple-input multiple-output (MIMO) systems with the aid of a reconfigurable intelligent surface (RIS), where the RIS enhances the primary communication via passive beamforming and at the same time delivers additional information by the spatial modulation (which adjusts the on-off states of the reflecting elements). For the passive beamforming design, we propose to maximize the sum channel capacity of the RIS-aided multiuser MIMO channel and formulate the problem as a two-step stochastic program. A sample average approximation (SAA) based iterative algorithm is developed for the efficient passive beamforming design of the considered scheme. To strike a balance between complexity and performance, we then propose a simplified beamforming algorithm by approximating the stochastic program as a deterministic alternating optimization problem. For the receiver design, the signal detection at the receiver is a bilinear estimation problem since the RIS information is multiplicatively modulated onto the reflected signals of the reflecting elements. To solve this bilinear estimation problem, we develop a turbo message passing (TMP) algorithm in which the factor graph associated with the problem is divided into two modules: one for the estimation of the user signals and the other for the estimation of the RIS's on-off states. The two modules are executed iteratively to yield a near-optimal low-complexity solution. Furthermore, we extend the design of the multiuser MIMO PBIT scheme from single-RIS to multi-RIS, by leveraging the similarity between the single-RIS and multi-RIS system models. Extensive simulation results are provided to demonstrate the advantages of our passive beamforming and receiver designs., 14 pages; 14 figures

From the co-array perspective, sparse spatial sampling can significantly increase the degrees-of-freedom (DOFs), enabling us to perform underdetermined direction-of-arrival (DOA) estimation. By leveraging the increased DOFs from the sparse spatial sampling, we develop a new underdetermined DOA estimation method for wideband signals, named wideband sparse spectrum fitting (W-SpSF) estimator. In W-SpSF, we formulate a sparse reconstruction problem that includes a quadratic $({\ell_2})$ weighted covariance fitting term added to a sparsity-promoting $({\ell _{2, 1}})$ regularizer. Meanwhile, the optimal regularization parameter of W-SpSF is studied to ensure robust sparse recovery. Numerical results enabled nested arrays demonstrate that the W-SpSF estimator outperforms the spatial smoothing based MUSIC algorithm and works well in nonuniform noise environment.

This paper reformulates the problem of direction-of-arrival (DOA) estimation for unknown nonuniform noise by exploiting a sparse representation of the array covariance vectors. In the proposed covariance sparsity-aware DOA estimator, the unknown noise variances can be eliminated by a linear transformation, and DOA estimation is reduced to a sparse reconstruction problem with nonnegativity constraint. The proposed method not only obtains an extended-aperture array with increased degrees of freedom which enables us to handle more sources than sensors, but also provides superiority in performance and robustness against nonuniform noise. Numerical examples under different conditions demonstrate the effectiveness of the proposed method.

This paper considers the sparse recovery problem of multiple measurement vector (MMV) model corrupted in impulsive noise. To ensure outlier-robust sparse recovery, we formulate an MMV problem that includes the generalized lp-norm (1 < p < 2) divergence data-fidelity term added to the l2,0 joint sparsity-promoting regularizer. The joint sparse penalty, however, is non-continuous and hence non-differentiable, which inevitably raises difficulty in optimization when using a gradient-based method. To address this, we build a smooth approximation for the l2,0-based sparse metric via the log-sum based sparse-encouraging surrogate function. Then, we propose a block successive upper-bound minimization algorithm for the smooth MMV problem by solving a series of subproblems based on the block coordinate descent (BCD) method. Furthermore, local convergence of the proposed algorithm to a stationary point of the smooth problem is proved. Experiments demonstrate its efficiency and robust recovery performance for suppressing impulsive noise.

In this paper, we consider secure downlink transmission in two-cell networks where the one base station (Alice) provides secure wireless services to its legitimate receiver (Bob) in the presence of a passive eavesdropper (Eve). For multi-cell networks, the inter-cell interference to Bob and Eve has a great impact on the security performance. In order to improve the confidentiality of information transfer from Alice to Bob, it is crucial to properly design the transmit strategies. With the results of conventional cooperative jamming (CJ) scheme, we propose a modified CJ scheme for cooperative base station (CBS) to further degrade the quality of signal reception at Eve, and therefore the secrecy rate can be increased. Both theoretical analysis and numerical results show that the modified CJ scheme outperforms the conventional CJ scheme by achieving a higher secrecy rate. Additionally, by taking the energy efficiency into account, we formulate an adaptive cooperation, in which the CBS is allowed to provide services to its desired receiver. We design a cooperative beamforming for CBS to satisfy the rate requirement while maximizing the secrecy rate of legitimate channel. Numerical results validate the effectiveness of the proposed cooperation scheme.

Most of the existing sparse recovery methods are based on the squared error criterion, i.e., l 2 -norm metric, by appropriately adding to a sparsity-promoting regularizer. This criterion is, however, statistically optimal only when the noise are Gaussian distributed. In fact, non-Gaussian impulsive noise with heavy tailed distribution has been reported in a variety of practical applications. To guarantee outlier-resistant sparse reconstruction for impulsive noise, in this paper we instead employ the generalized l p -norm (1 ≤ p p − l 0 sparse recovery problem, where the reweighted matrices constructed from the previous iterative solution are considered both for l p and l 0 metrics. Simulation results demonstrate the efficiency and robustness of the proposed algorithms.