Integrated sensing and communication (ISAC) is viewed as a key technology in future wireless networks. One of the main challenges in realizing ISAC is developing dual-functional waveforms that can communicate with com...
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Integrated sensing and communication (ISAC) is viewed as a key technology in future wireless networks. One of the main challenges in realizing ISAC is developing dual-functional waveforms that can communicate with communication receivers and perform radar sensing simultaneously. In this paper, we consider the joint design of a dual-functional orthogonal time-frequency space (OTFS) signal and a receiving filter for the ISAC system. The problem of ISAC waveform design is formulated as the minimization of the weighted integrated sidelobe level (WISL) of the ambiguity function and the interference term from ISAC waveform, with constraints on signal-to-noise ratio loss. The majorization-minimization algorithm combined with alternating iterative minimization is implemented to solve the optimization problem. Simulation results show that the WISL and the interference term can be significantly decreased to guarantee achievable data rates and detection performance.
Nonnegative matrix factorization (NMF) is a dimension reduction and clustering tech-nique for data analysis which has been widely used in image processing, text analysis and hyperspectral decomposition because of its ...
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Nonnegative matrix factorization (NMF) is a dimension reduction and clustering tech-nique for data analysis which has been widely used in image processing, text analysis and hyperspectral decomposition because of its stronger practical significance and better interpretability. Approximate matrix factorization techniques with both nonnegativity and orthogonality constraints, referred to as orthogonal NMF (ONMF), have been shown to work remarkably better for clustering tasks than NMF. At present, a large number of algorithms have been used to solve the ONMF problems, but these methods usually cannot take into account the classification accuracy and calculation speed. In this paper, we propose a new form of penalized NMF with orthogonal regularization that combines the decomposition residual minimization based on the Euclidean distance and the orthogonality maximization based on the Kullback-Leibler divergence. This paper uses majorization-minimization (MM) method by minimizing a majorization function of the original problem and obtains a new iterative scheme (MM-ONMF). Comparing with several traditional ONMF methods on eight datasets, experimental results show that the proposed method has better clustering results and less computing time. (c) 2022 Elsevier B.V. All rights reserved.
作者：
Wang, ChenFang, YongShanghai Univ
Shanghai Inst Adv Commun & Data Sci Joint Int Res Lab Specialty Fiber Opt & Adv Commu Key Lab Specialty Fiber Opt & Opt Access Networks Shanghai 200444 Peoples R China
In this paper, we study the sparse signal recovery that uses information of both support and amplitude of the sparse signal. A convergent iterative algorithm for sparse signal recovery is developed using majorization-...
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In this paper, we study the sparse signal recovery that uses information of both support and amplitude of the sparse signal. A convergent iterative algorithm for sparse signal recovery is developed using majorization-minimization-based Non-convex Optimization (MM-NcO). Furthermore, it is shown that, typically, the sparse signals that are recovered using the proposed iterative algorithm are not globally optimal and the performance of the iterative algorithm depends on the initial point. Therefore, a modified MM-NcO-based iterative algorithm is developed that uses prior information of both support and amplitude of the sparse signal to enhance recovery performance. Finally, the modified MM-NcO-based iterative algorithm is used to estimate the time-varying sparse wireless channels with temporal correlation. The numerical results show that the new algorithm performs better than related algorithms.
This letter investigates the uplink of a multi-user millimeter wave (mmWave) system, where the base station (BS) is equipped with a massive multiple-input multiple-output (MIMO) array and resolution-adaptive analog-to...
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This letter investigates the uplink of a multi-user millimeter wave (mmWave) system, where the base station (BS) is equipped with a massive multiple-input multiple-output (MIMO) array and resolution-adaptive analog-to-digital converters (RADCs). Although employing massive MIMO at the BS can significantly improve the spectral efficiency, it also leads to high hardware complexity and huge power consumption. To overcome these challenges, we seek to jointly optimize the beamspace hybrid combiner and the ADC quantization bits allocation to maximize the system energy efficiency (EE) under some practical constraints. The formulated problem is non-convex due to the non-linear fractional objective function and the non-convex feasible set which is generally intractable. In order to handle these difficulties, we first apply some fractional programming (FP) techniques and introduce auxiliary variables to recast this problem into an equivalent form amenable to optimization. Then, we propose an efficient double-loop iterative algorithm based on the penalty dual decomposition (PDD) and the majorization-minimization (MM) methods to find local stationary solutions. Simulation results reveal significant gain over the baselines.
Focused energy delivery, which can precisely transmit energy to the target area, is the key technology in precision electronic warfare (PREW). In practical applications, focused energy delivery technology is generally...
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Focused energy delivery, which can precisely transmit energy to the target area, is the key technology in precision electronic warfare (PREW). In practical applications, focused energy delivery technology is generally designed for ultra-sparse arrays. However, high computational complexity and severe grating lobe effect are still two main challenges in this field, especially when focused energy delivery is applied for complicated environments and multiple moving platforms. In this paper, we aim at designing highly efficient focused energy delivery technique with low grating lobe levels for ultra-sparse sensor arrays. Unlike existing methods that build models via estimating the covariance matrix of the transmitted signal, we directly establish array signal estimation models and consider the grating lobe effect in the objective function by proposing two metrics. To solve proposed models, we develop three efficient algorithms under the framework of majorization-minimization (MM) and alternating direction method of multiplier (ADMM). These algorithms all have low computational complexity and can achieve tighter upperbounds. Numerical simulations show that, the proposed algorithms are computationally more efficient and can alleviate the grating lobe effect compared to state-of-art algorithms. (C) 2019 Elsevier B.V. All rights reserved.
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