In this paper, we propose a time-division integrated sensing, communication and control (ISCC) scheme designed to dynamically enhance communication and sensing capabilities on the UAV platform. The UAV is dispatched to track a randomly moving target for capturing and transmitting sensing data to the base station via wireless communication. The goal is to leverage the ISCC framework for maximizing the cumulative sensing mutual information while guaranteeing successful data transmission by optimizing the allocation of the communication and sensing time slots together with the UAV’s control scheme. The formulated problem cannot be straightforwardly solved by off-the-shelf optimization algorithms due to the time-varying environment. To tackle this challenge, a constrained soft actor-critic (C-SAC) algorithm is developed, which dynamically switches between maximizing rewards and minimizing constraint violations to ensure robust performance in changing environments while maintaining the simplicity and efficiency of unconstrained policy optimization. Simulation results demonstrate that the proposed C-SAC algorithm outperforms dual-variable-based methods in handling the constrained problems, while extensive Monte Carlo tests confirm the robustness of the ISCC policy trained by the proposed algorithm, which adapts to varying target speeds and achieves higher cumulative mutual information compared to the point-mass UAV models.

In this article, a joint design of communication sensing and control (JDCSC) scheme is developed which focuses on a scenario where a cellular-connected unmanned aerial vehicles (UAV) senses a moving target. The goal is to maximize the sensing mutual information via jointly optimizing the transmit power, the trajectory of the UAV and the task completion time, while meeting the onboard energy, the communication service quality, and the UAV flight safety constraints. In particular, UAV dynamics are considered, which are usually ignored in the existing design and inferior communication and sensing quality of service might be resulted. The formulated problem is dynamic optimization problem, which is difficult to be solved. The control parameterization method and exact penalty function scheme are utilized to transform the problem into a static nonlinear program which can be solved by gradient-based methods. The effectiveness of the JDCSC approach is verified by carrying out some numerical examples.

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 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 paper investigates the passive beamforming and information transfer (PBIT) technique for multiuser multiple-input multiple-output (Mu-MIMO) systems with the aid of reconfigurable intelligent surfaces (RISs), where the RISs enhance the primary communication via passive beamforming (P-BF) and at the same time deliver additional information by the on-off reflecting modulation (in which the RIS information is carried by the on/off state of each reflecting element). For the P-BF design, we propose to maximize the achievable user sum rate of the RIS-aided Mu-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 P-BF design of the considered scheme. To strike a balance between complexity and performance, we further propose a simplified P-BF 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 on-off state of each RIS element. The two modules are executed iteratively to yield a near-optimal low-complexity solution. Furthermore, we extend the design of the Mu-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 P-BF 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 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 ℓpnorm(02approach 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 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 (12,0joint 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.

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.