Search Results (490)

This study presents a method for deriving closed-form solutions for Lagrange multipliers in worst-case performance optimization (WCPO) beamforming. By approximating the array-received signal autocorrelation matrix as a rank-1 Hermitian matrix using the low-rank approximation theory, analytical expressions for the Lagrange multipliers are derived. The method was first developed for a single plane wave scenario and then generalized to multiplane wave cases with an autocorrelation matrix rank of N. Simulations demonstrate that the proposed Lagrange multiplier formula exhibits a performance comparable to that of the second-order cone programming (SOCP) method in terms of signal-to-interference-plus-noise ratio (SINR) and direction-of-arrival (DOA) estimation accuracy, while offering a significant reduction in computational complexity. The proposed method requires three orders of magnitude less computation time than the SOCP and has a computational efficiency similar to that of the diagonal loading (DL) technique, outperforming DL in SINR and DOA estimations. Fourier amplitude spectrum analysis revealed that the beamforming filters obtained using the proposed method and the SOCP shared frequency distribution structures similar to the ideal optimal beamformer (MVDR), whereas the DL method exhibited distinct characteristics. The proposed analytical expressions for the Lagrange multipliers provide a valuable tool for implementing robust and real-time adaptive beamforming for practical applications. Full article

This paper proposes a distributed model predictive control (DMPC) framework integrating an event-triggered mechanism and an adaptive alternating direction method of multipliers (ADMM) to address the challenges of constrained computational resources and stringent real-time requirements in distributed vehicle platoon control systems. Firstly, the longitudinal dynamic model and communication topology of the vehicle platoon are established. Secondly, under the DMPC framework, a controller integrating residual-based adaptive ADMM and an event-triggered mechanism is designed. The adaptive ADMM dynamically adjusts the penalty parameter by leveraging residual information, which significantly accelerates the solving of the quadratic programming (QP) subproblems of DMPC and ensures the real-time performance of the control system. In order to reduce unnecessary solver invocations, the event-triggered mechanism is employed. Finally, numerical simulations verify that the proposed control strategy significantly reduces both the computation time per optimization and the cumulative optimization instances throughout the process. The proposed approach effectively alleviates the computational burden on onboard resources and enhances the real-time performance of vehicle platoon control. Full article

Accurate node localization is essential for many Internet of Things (IoT) applications. However, incomplete and noisy distance measurements often degrade the reliability of the Euclidean Distance Matrix (EDM), which is critical for range-based localization. To address this issue, a Low-Rank Matrix Completion approach based on nonconvex rank approximation (LRMCN) is proposed to recover the true EDM. First, the observed EDM is decomposed into a low-rank matrix representing the true distances and a sparse matrix capturing noise. Second, a nonconvex surrogate function is used to approximate the matrix rank, while the $l_1$-norm is utilized to model the sparsity of the noise component. Third, the resulting optimization problem is solved using the Alternating Direction Method of Multipliers (ADMMs). This enables accurate recovery of a complete and denoised EDM from incomplete and corrupted measurements. Finally, relative node locations are estimated using classical multi-dimensional scaling, and absolute coordinates are determined based on a small set of anchor nodes with known locations. The experimental results show that the proposed method achieves superior performance in both matrix completion and localization accuracy, even in the presence of missing and corrupted data. Full article

This paper addresses the NP-hard problem of solving the rank of a matrix in Robust Principal Component Analysis (RPCA) by proposing a nonconvex fractional regularization approximation. Compared to existing convex regularization (which often yields suboptimal solutions) and nonconvex regularization (which typically requires parameter selection), the proposed model effectively avoids parameter selection while preserving scale invariance. By introducing an auxiliary variable, we transform the problem into a nonconvex optimization problem with a separable structure. We use a more flexible Symmetric Alternating Direction Method of Multipliers (SADMM) to arrive at a solution and provide a rigorous convergence proof. In numerical experiments involving synthetic data, image recovery, and foreground–background separation for surveillance video, the proposed fractional regularization model demonstrates high computational accuracy, and its performance is comparable to that of many state-of-the-art algorithms. Full article

Maritime Internet of Things (IoT) with unmanned surface vessels (USVs) faces tight onboard computing and sparse wireless links. Compute-intensive vision and sensing workloads often exceed latency budgets, which undermines timely decisions. In this paper, we propose a novel distributed computation offloading framework for maritime IoT scenarios. By leveraging the limited computational resources of USVs within a device-to-device (D2D)-assisted edge network and the mobility advantages of UAV-assisted edge computing, we design a breadth-first search (BFS)-based distributed computation offloading game. Building upon this, we formulate a global latency minimization problem that jointly optimizes UAV hovering coordinates and arrival times. This problem is solved by decomposing it into subproblems addressed via a joint Alternating Direction Method of Multipliers (ADMM) and Successive Convex Approximation (SCA) approach, effectively reducing the time between UAV arrivals and hovering coordinates. Extensive simulations verify the effectiveness of our framework, demonstrating up to a 49.6% latency reduction compared with traditional offloading schemes. Full article

To enhance the spectral efficiency of hybrid beamforming in millimeter-wave massive MIMO systems, the problem is formulated as a high-dimensional non-convex optimization under constant modulus constraints. A novel algorithm based on fourth-order tensor Tucker decomposition is proposed. Specifically, the frequency-domain channel matrices are structured into a fourth-order tensor to explicitly capture the couplings across the spatial, frequency, and user domains. To tackle the non-convexity induced by constant modulus constraints, the analog precoder and combiner are derived by solving a truncated-rank Tucker decomposition problem through the Alternating Direction Method of Multipliers and Alternating Least Squares schemes. Subsequently, in the digital domain, the Regularized Block Diagonalization algorithm is integrated with the subcarrier and user factor matrices—obtained from the tensor decomposition—along with the water-filling strategy to design the digital precoder and combiner, thereby achieving a balance between multi-user interference suppression and noise enhancement. The proposed tensor-based algorithm is demonstrated through simulations to outperform existing state-of-the-art schemes. This work provides an efficient and mathematically sound solution for hybrid beamforming in dense multi-user scenarios envisioned for sixth-generation mobile communications. Full article

This paper focuses on two-dimensional (2-D) direction-of-arrival (DOA) estimation for an L-shaped array. While recent studies have explored sparse methods for this problem, most exploit only the cross-correlation matrix, neglecting self-correlation information and resulting accuracy degradation. We propose a multidimensional matrix completion method that employs joint sparsity and redundant correlation information embedded in the covariance matrix to reconstruct a structured matrix compactly coupling the two DOA parameters. A semidefinite program problem formulated via covariance fitting criteria is proved equivalent to the atomic norm minimization framework. The alternating direction method of multipliers is designed to reduce computational costs. Numerical results corroborate the analysis and demonstrate the superior estimation accuracy, identifiability, and resolution of the proposed method. Full article

Streak Tube Imaging Lidar (STIL) offers significant advantages in long-range sensing and ultrafast diagnostics by encoding spatial-temporal information as streaks, and hence decodes 3D images using tailored algorithm. However, under low-photon conditions that caused either long-range or reduced exposure time, the reconstructed image suffer from low contrast, strong noise and blurring, hindering the application in various scenarios. To address this challenge, we propose a Multi-Sparsity Constraints and Adaptive Regularization (MSC-AR) algorithm based on the Maximum a Posteriori (MAP) framework, which jointly denoises and deblurs degraded streak images and efficiently solved using the Alternating Direction Method of Multipliers (ADMM). MSC-AR considers gradient sparsity, intensity sparsity, and an adaptively weighted Total Variation (TV) regularization along the temporal dimension of the streak image which collaboratively optimizing image quality and structural detail, thus better 3D restoration results in low-photon conditions. Experimental results demonstrate that MSC-AR significantly outperforms existing approaches under low-photon conditions. At an exposure time of 300 ms, it achieves millimeter-level RMSE and over 88% SSIM in depth image reconstruction, while maintaining robustness and generalization across different reconstruction strategies and target types. Full article

To further improve the economic benefits of operators and the low-carbon performance within the system, this paper proposes a hierarchical distributed low-carbon economic dispatch strategy for regional integrated energy systems (RIESs) based on the Alternating Direction Method of Multipliers (ADMM). First, the energy coupling relationships among conversion devices in RIESs are analyzed, and a structural model of RIES incorporating an energy generation operator (EGO) and multiple load aggregators (LAs) is established. Second, considering the stepwise carbon trading mechanism (SCTM) and the average thermal comfort of residents, economic optimization models for operators are developed. To ensure optimal energy trading strategies between conflicting stakeholders, the EGO and LAs are embedded into a master–slave game trading framework, and the existence of the game equilibrium solution is rigorously proven. Furthermore, considering the processing speed of the optimization problem by the operators and the operators’ data privacy requirement, the optimization problem is solved in a hierarchical distributed manner using ADMM. To ensure the convergence of the algorithm, the non-convex feasible domain of the subproblem bilinear term is transformed into a convex polyhedron defined by its convex envelope so that the problem can be solved by a convex optimization algorithm. Finally, an example analysis shows that the scheduling strategy proposed in this paper improves the economic efficiency of energy trading participants by 3% and 3.26%, respectively, and reduces the system carbon emissions by 10.5%. Full article

In the context of the coordinated operation of microgrids and community energy storage systems, achieving optimal resource allocation under complex and uncertain conditions has emerged as a prominent research focus. This study proposes a robust collaborative optimization model for microgrids and community energy storage systems under a game-theoretic environment where potential fraudulent behavior is considered. A multi-energy collaborative system model is first constructed, integrating multiple uncertainties in source-load pricing, and a max-min robust optimization strategy is employed to improve scheduling resilience. Secondly, a game-theoretic model is introduced to identify and suppress manipulative behaviors by dishonest microgrids in energy transactions, based on a Nash bargaining mechanism. Finally, a distributed collaborative solution framework is developed using the Alternating Direction Method of Multipliers and Column-and-Constraint Generation to enable efficient parallel computation. Simulation results indicate that the framework reduces the alliance’s total cost from CNY 66,319.37 to CNY 57,924.89, saving CNY 8394.48. Specifically, the operational costs of MG1, MG2, and MG3 were reduced by CNY 742.60, CNY 1069.92, and CNY 1451.40, respectively, while CES achieved an additional revenue of CNY 5130.56 through peak shaving and valley filling operations. Furthermore, this distributed algorithm converges within 6–15 iterations and demonstrates high computational efficiency and robustness across various uncertain scenarios. Full article

Model predictive control (MPC) is a commonly used online strategy for maximizing economic benefits in smart homes that integrate photovoltaic (PV) panels, electric vehicles (EVs), and battery energy storage systems (BESSs). However, prediction errors associated with PV power and load demand can lead to economic losses. Scenario-based MPC can mitigate the impact of prediction errors by computing the expected objective value of multiple stochastic scenarios. However, reducing the number of scenarios is often necessary to lower the computation burden, which in turn causes some economic loss. To achieve online operation and maximize economic benefits, this paper proposes utilizing the consensus alternating direction method of multipliers (C-ADMM) algorithm to quickly calculate the scenario-based MPC problem without reducing stochastic scenarios. First, the system layout and relevant component models of smart homes are established. Then, the stochastic scenarios of net load prediction error are generated through Monte Carlo simulation. A consensus constraint is designed about the first control action in different scenarios to decompose the scenario-based MPC problem into multiple sub-problems. This allows the original large-scale problem to be quickly solved by C-ADMM via parallel computing. The relevant results verify that increasing the number of stochastic scenarios leads to more economic benefits. Furthermore, compared with traditional MPC with or without prediction error, the results demonstrate that scenario-based MPC can effectively address the economic impact of prediction error. Full article

Against the backdrop of global low-carbon transition, the integrated development of electricity and carbon markets demands higher efficiency in the optimal operation of virtual power plants (VPPs) and distribution networks, yet conventional trading mechanisms face limitations such as inadequate recognition of differentiated contributions and inequitable benefit allocation. To address these challenges, this paper proposes a collaborative optimal trading mechanism for VPP clusters and distribution networks in an electricity–carbon coupled market environment by first establishing a joint operation framework to systematically coordinate multi-agent interactions, then developing a bi-level optimization model where the upper level formulates peer-to-peer (P2P) trading plans for electrical energy and carbon allowances through cooperative gaming among VPPs while the lower level optimizes distribution network power flow and feeds back the electro-carbon comprehensive price (EACP). By introducing an asymmetric Nash bargaining model for fair benefit distribution and employing the Alternating Direction Method of Multipliers (ADMM) for efficient computation, case studies demonstrate that the proposed method overcomes traditional models’ shortcomings in contribution evaluation and profit allocation, achieving 2794.8 units in cost savings for VPP clusters while enhancing cooperation stability and ensuring secure, economical distribution network operation, thereby providing a universal technical pathway for the synergistic advancement of global electricity and carbon markets. Full article

Spectrum map fusion has emerged as an effective technique to enhance the accuracy of spectrum map construction. However, many existing fusion methods fail to address the strong correlation between spectrum data, resulting in sub-optimal performance. In this paper, we propose a new multi-unmanned aerial vehicle (UAV) spectrum map fusion method based on differential ridge regression. We first construct spectrum maps of UAVs by using differential features of spectrum data. Next, we present a spectrum map fusion model by leveraging the spatial distribution characteristic of spectrum data. To reduce the sensitivity of the fusion model to the strongly correlated data, a new map fusion regularization term is designed, which introduces $l_2$-norm to constrain the fusion regularization parameters and compress the ridge regression coefficient sizes. As a result, accurate spectrum maps can be constructed for the environments with highly correlated spectrum data. We then formulate a model-driven solution to the spectrum map fusion problem and derive its lower bound. By combining the propagation characteristics of the spectrum signal with the developed Lagrange duality, we can guarantee the convergence of map fusion processing while enhancing the convergence rate. Finally, we propose an accelerated maximally split alternating directions method of multipliers (AMS-ADMM) to reduce the computational complexity of spectrum map construction. Simulation results demonstrate that our proposed method can effectively eliminate external noise interference and outliers, and achieve an accuracy improvement of more than 27% compared to state-of-the-art fusion methods in spectrum map construction with low complexity. Full article

Shared energy storage (SES) systems, operating alongside microgrid clusters, can effectively mitigate power fluctuations and reduce the operational costs of independently constructed energy storage systems. Consequently, capacity allocation planning for SES in microgrid clusters has emerged as a crucial technology for achieving the system’s economical and efficient operation. This paper presents a two-layer optimal allocation model utilizing the Alternating Direction Method of Multipliers (ADMMs) to characterize system operation precisely. By establishing a refined mathematical model of a microgrid cluster with SES and analyzing the energy flow interaction mechanisms inside the cluster, along with the configuration scheme for SES capacity. The upper layer optimization of the model minimizes operational and maintenance investment costs associated with designing the capacity of SES, while the lower layer model optimizes the operation scheduling with the goal of the lowest operation cost. To illustrate the efficacy and benefits of the proposed method, case studies are conducted in different scenarios comparing the proposed method with the conventional method to analyze the power distribution features of the microgrid and the allocation planning of shared energy storage capacity. Full article

The promotion of local renewable energy consumption and stable power gird (the latter is referred to as PG) operation have emerged as the primary objectives of power system reform. The integration of multiple microgrids with distinct characteristics through the utilization of shared energy storage (the following is referred to as SES) facilitates coordinated operation. This approach enables the balancing of energy across temporal and spatial domains, contributing to the overall reliability and security of the energy network. The proposed model outlines a methodology for the coordinated operation of multiple microgrids and SES, with a focus on asymmetric price negotiation. Initially, cost and revenue models for microgrids and SES power plants are established. Secondly, an asymmetric pricing method based on the magnitude of each entity’s energy contribution is proposed. A profit optimization model is also established. The model can be decomposed into two distinct subproblems: the maximization of overall profit and the negotiation of transaction prices. The model can be solved by employing the alternating direction method of multipliers (ADMM). Finally, a series of case studies were conducted for the purpose of validating the operation optimization model that was previously constructed. These studies demonstrate that the model enhances collective operational efficiency by 44.69%, with each entity’s efficiency increasing by at least 12%. At the same time, cooperative benefits are distributed fairly according to each entity’s energy contribution. Full article