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Decentralized Robust Transceiver Designs for MISO SWIPT Interference Channel

IEEE ACCESS 2018年 6卷 4537-4546页

作者： Wang, Feng Peng, Tao Huang, Yongwei Guangdong Univ Technol Sch Informat Engn Guangzhou 510006 Guangdong Peoples R China Fudan Univ Dept Commun Sci & Engn Shanghai 200433 Peoples R China

This paper considers a K-user multiple-input single-output interference channel for simultaneous wireless information and power transfer, where each multi-antenna transmitter serves a single antenna receiver per user pair. All receivers perform simultaneously information processing and energy harvesting (EH) based on the receive power-splitting (PS) architectures. Assuming imperfect channel state information (CSI) at the transmitters, we develop a near-optimal robust transceiver design scheme that minimizes the total transmission power under the worst-case signal-to-interference-plus-noise ratio (SINR) and EH constraints at the receivers, by jointly optimizing transmit beamforming and receive PS ratio per receiver. When the CSI uncertainties are bounded by ellipsoidal regions, it is shown that the worst-case SINR and EH constraints per receiver can be recast into quadratic matrix inequality forms. Leveraging semidefinite relaxation technique, the intended robust beamforming and PS (BFPS) problem can be relaxed as a tractable (centralized) semidefinite program. More importantly, relying on the state-of-the-art alternating direction method of multipliers in convex optimization, we propose a decentralized algorithm capable of computing the robust BFPS scheme with local CSI and limited information exchange among the transmitters. It is shown that the proposed decentralized algorithm is guaranteed to converge to the centralized solution. Numerical results are provided to demonstrate the merits of the proposed approaches.

关键词： Interference channels energy harvesting power-splitting robust beamforming admm algorithm

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Joint rate control and power allocation for low-latency reliable D2D-based relay network

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EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 2019年第1期2019卷 1页

作者： Wang, Yahui He, Yanhua Xu, Chen Zhou, Zhenyu Mumtaz, Shahid Rodriguez, Jonathan Pervaiz, Haris North China Elect Power Univ Sch Elect & Elect Engn State Key Lab Alternate Elect Power Syst Renewabl Beijing Peoples R China Inst Telecommun P-3810193 Aveiro Portugal Univ South Wales Pontypridd CF37 1DL M Glam Wales Univ Lancaster SCC Pontypridd M Glam Wales

Emerging 5G applications impose stringent requirements on network latency and reliability. In this work, we propose a low-latency reliable device-to-device (D2D) relay network framework to improve the cell coverage and user satisfaction. Particularly, we develop a cross-layer low-complexity resource allocation algorithm, which jointly optimizes the rate control and power allocation from a long-term perspective. The long-term optimization problem is transformed into a series of short-term subproblems by using Lyapunov optimization, and the objective function is separated into two independent subproblems related to rate control in network layer and power allocation in physical layer. Next, the Karush-Kuhn-Tucher (KKT) conditions and alternating direction method of multipliers (admm) algorithm are employed to solve the rate control subproblem and power allocation subproblem, respectively. Finally, simulation results demonstrate that the proposed algorithm can reach 99.9% of the optimal satisfaction of D2D pairs with lower average network delay compared to the baseline algorithm. Furthermore, the convergence time of the admm-based power allocation algorithm is only about 1.7% of that by using the CVX toolbox.

关键词： D2D-based relay network Rate control Power allocation Lyapunov optimization admm algorithm Low-latency and reliability

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Min Flow Rate Maximization for Software Defined Radio Access Networks

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IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS 2014年第6期32卷 1282-1294页

作者： Liao, Wei-Cheng Hong, Mingyi Farmanbar, Hamid Li, Xu Luo, Zhi-Quan Zhang, Hang Univ Minnesota Dept Elect & Comp Engn Minneapolis MN 55455 USA Huawei Technol Ottawa Res & Dev Ctr Ottawa ON L3R 5A4 Canada

We consider a cloud-based heterogeneous network of base stations (BSs) connected via a backhaul network of routers and wired/wireless links with limited capacity. The optimal provision of such networks requires proper resource allocation across the radio access links in conjunction with appropriate traffic engineering within the backhaul network. In this paper, we propose an efficient algorithm for joint resource allocation across the wireless links and flow control over the entire network. The proposed algorithm, which maximizes the min-rate among all the transmitted commodities, is based on a decomposition approach that leverages both the alternating direction method of multipliers (admm) and the weighted-MMSE (WMMSE) algorithm. We show that this algorithm is easily parallelizable and converges globally to a stationary solution of the joint optimization problem. The proposed algorithm can also be extended to networks with multi-antenna nodes and other utility functions.

关键词： Heterogeneous networks admm algorithm software defined networking cross-layer optimization small cell limited backhaul

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Enforcing Geometrical Priors in Deep Networks for Semantic Segmentation Applied to Radiotherapy Planning

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JOURNAL OF MATHEMATICAL IMAGING AND VISION 2022年第8期64卷 892-915页

作者： Lambert, Zoe Le Guyader, Carole Petitjean, Caroline Normandie Univ INSA Rouen LMI Rouen France Normandie Univ UNIHAVRE UNIROUEN INSA RouenLITIS Rouen France

Incorporating prior knowledge into a segmentation process, whether it is geometrical constraints such as volume penalisation, (partial) convexity enforcement, or topological prescriptions to preserve the contextual relations between objects, proves to improve accuracy in medical image segmentation, in particular when addressing the issue of weak boundary definition. Motivated by this observation, the proposed contribution aims to provide a unified variational framework including geometrical constraints in the training of convolutional neural networks in the form of a penalty in the loss function. These geometrical constraints take several forms and encompass level curve alignment through the integration of the weighted total variation, an area penalisation phrased as a hard constraint in the modelling, and an intensity homogeneity criterion based on a combination of the standard Dice loss with the piecewise constant Mumford-Shah model. The mathematical formulation yields a non-smooth non-convex optimisation problem, which rules out conventional smooth optimisation techniques and leads us to adopt a Lagrangian setting. The application falls within the scope of organ-at-risk segmentation in CT (computed tomography) images, in the context of radiotherapy planning. Experiments demonstrate that our method provides significant improvements (i) over existing non-constrained approaches both in terms of quantitative criteria, such as the measure of overlap, and qualitative assessment (spatial regularisation/coherency, fewer outliers), (ii) over in-layer constrained deep convolutional networks, and shows a certain degree of versatility.

关键词： Image segmentation Deep learning Proximal operator admm algorithm Douglas-Rachford algorithm Primal-dual algorithm

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Wideband MIMO Radar Waveform Design

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2019年第13期67卷 3487-3501页

作者： Yu, Xianxiang Cui, Guolong Yang, Jing Kong, Lingjiang Li, Jian Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Sichuan Peoples R China Univ Sci & Technol China Dept Elect Engn & Informat Sci Hefei 230052 Anhui Peoples R China Univ Florida Dept Elect & Comp Engn Gainesville FL 32611 USA

We consider the design of wideband multiple-input multiple-output (MIMO) radar waveforms to approximate a desired beampattern and achieve prescribed space-frequency nulling in a space-frequency region of interest. A general design model involving modulus, power, and energy constraints on the probing waveforms is introduced. To deal with the resulting non-convex design, a novel iterative algorithm with low computational complexity is proposed. In each iteration, the non-convex design is approximated successively through a series of convex subproblems. Then, each convex subproblem is reformulated in a new alternating direction method of multipliers form, with small-sized subproblems solved in parallel with closed-form solutions via utilizing the Karush-Kuhn-Tucker conditions. Numerical results are provided to demonstrate the effectiveness of the proposed algorithm.

关键词： Wideband waveform design multiple-input multiple-output (MIMO) radar beampattern design space-frequency nulling admm algorithm

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k Block Sparse Vector Recovery via Block l1 - l2 Minimization

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CIRCUITS SYSTEMS AND SIGNAL PROCESSING 2023年第5期42卷 2897-2915页

作者： Xie, Shaohua Liang, Kaihao Sun Yat Sen Univ Sch Math 135 Xin Gang Xi Lu Guangzhou 510275 Guangdong Peoples R China Zhongkai Univ Agr & Engn Dept Math 501 Zhongkai Rd Guangzhou 510225 Guangdong Peoples R China

In this paper, k block sparse vectors are studied, and the block l(1) - l(2) model is adopted. It is proved theoretically that when the block sparsity satisfies some conditions, the k block sparse vector can be accurately recovered by the noise free block l(1) - l(2) model, and it can also be stably recovered by the noisy block l(1) - l(2) model. In the algorithm, we use the convex difference algorithm, and prove that the aggregation points of the sequence generated by the algorithm converge to the stable point of the objective function. We prove that when the parameter lambda > 0 is less than a certain number lambda(k), the aggregation points of the sequence generated by the algorithm are block sparse. Finally, we conduct data experiments. The experiments show that when the vector is block sparse, the block l(1) - l(2) model can recover the unknown vector better than the traditional l(1) - l(2) model.

关键词： Compressed sensing k block sparse vector Block l(1) - l(2) minimization DCA algorithm admm algorithm

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A distributed sparse logistic regression with L1/2 regularization for microarray biomarker discovery in cancer classification

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SOFT COMPUTING 2023年第5期27卷 2537-2552页

作者： Ai, Ning Yang, Ziyi Yuan, Haoliang Ouyang, Dong Miao, Rui Ji, Yuhan Liang, Yong Macau Univ Sci & Technol Sch Comp Sci & Engn Fac Innovat Engn Ave Wai Long Taipa Macao Peoples R China Tencent Quantum Lab Shenzhen 518057 Guangdong Peoples R China Guangdong Univ Technol Sch Automat Guangzhou 510006 Peoples R China Peng Cheng Lab Shenzhen 518005 Peoples R China

Microarray is a high-throughput sequencing technology, which can be used to classify cancer types and select the highly relevant cancer biomarkers (i.e., genes). To improve the availability of ever-increasing microarray data, data-integrative analysis becomes a hot research direction. However, the complexity of gene expression data still brings many challenges to the data integration methods: (1) the relevant biomarker selection in multiple high-dimensional datasets;(2) the batch effects between datasets;(3) the high noise in features and samples;(4) the large-scale data analysis with high computational cost. To overcome these challenges, we propose a novel Distribute-based Biological data-Integrative Analysis model-DBIA. DBIA is based on the L-1/2 regularized logistic regression (L-1/2 LR) model and the alternating direction multiplication algorithm (admm) for data integration. The regularization model is an effective method for selecting latent cancer-relevant genes and improving the accuracy of cancer classification. Moreover, we adopt the L-1/2 LR model to reduce the noise and dimensionality of the data. admm is employed to reduce the batch effects between datasets, analyze multiple datasets in parallel, and save the computational cost of large-scale data analysis. Experimental results on the simulation and real-world datasets demonstrate that DBIA achieves the good prediction performance with a shorter time, lower hardware requirements, and strong robustness. The genes selected by DBIA have a certain biological significance.

关键词： Microarray data integration L-1/2 regularized logistic regression admm algorithm Cancer classification Gene selection

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Estimation and identification of latent group structures in panel data

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JOURNAL OF ECONOMETRICS 2023年第2期235卷 1464-1482页

作者： Mehrabani, Ali Southern Illinois Univ Coll Business & Analyt Carbondale IL 62901 USA

This paper provides a framework for joint estimation and identification of latent group structures in panel data models using a pairwise fusion penalized approach. The latent structure of the model allows individuals to be classified into different groups where the number of groups and the group membership are unknown. The individuals within a group have common slope parameters, while parameter heterogeneity is allowed across the groups. A penalized least squares (PLS) approach is introduced for models with exogenous regressors. When the model contains endogenous regressors, a penalized generalized method of moment (PGMM) is introduced. To implement the proposed approach, an alternating direction method of multipliers algorithm has been developed. The proposed method is further illustrated by simulation studies which demonstrate the finite sample performance of the method, and is applied in an empirical analysis. & COPY;2022 Elsevier B.V. All rights reserved.

关键词： admm algorithm Classification Dynamic panel High dimensionality Oracle property Pairwise adaptive group fused Lasso Parameter heterogeneity

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A Unified Framework for Epidemic Prediction based on Poisson Regression

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2015年第11期27卷 2878-2892页

作者： Zhang, Yu Cheung, William K. Liu, Jiming Hong Kong Baptist Univ Dept Comp Sci Hong Kong Peoples R China Hong Kong Baptist Univ Inst Res & Continuing Educ Hong Kong Peoples R China Hong Kong Baptist Univ Dept Comp Sci Hong Kong Hong Kong Peoples R China

Epidemic prediction is an important problem in epidemic control. Poisson regression methods are often adopted in existing works, mostly with only the (intra-)regional environmental factors considered. As the diffusion of epidemics is affected by not only the intra-regional factors but also inter-regional and external ones, a unified framework based on Poisson regression with the three types of factors incorporated is proposed for the prediction. Specifically, we propose a Poisson-regression-based model first with the intra-regional and inter-regional factors included. The intra-regional factor in a particular time interval is represented by one feature vector with the regionally environmental and social factors considered. The inter-regional factor is modeled by a diffusion matrix which describes the possibilities that the epidemics can spread from one region to another, which in turn accounts for the propagating effects of the infected cases. To learn the structure of the diffusion matrix, we propose two approaches-utilizing some a priori knowledge (e.g., transportation network) and estimating it from scratch via a sparse structure assumption. The resulting optimization problem of the maximum a posterior solution is a convex one and can be efficiently solved by the alternating direction method of multipliers (admm). In addition, we incorporate also the external factor, i.e., the imported cases. With one fact that the distribution of the number of infected cases over a year is (approximately) unimodal for most epidemics and one assumption that the importing rate has a small variance over the year, we can approximate the effect of the external factor with a parametric function (e.g., a quadratic function) over time. The resulting optimization problem is still convex and can be also solved by the admm algorithm. Empirical evaluations are conducted based on a real data set which records the 16-days-reported cases in the Yunnan province of China for seven years, from 20

关键词： Epidemic prediction Poisson regression admm algorithm

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Nonconvex Robust High-Order Tensor Completion Using Randomized Low-Rank Approximation

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2024年 33卷 2835-2850页

作者： Qin, Wenjin Wang, Hailin Zhang, Feng Ma, Weijun Wang, Jianjun Huang, Tingwen Southwest Univ Sch Math & Stat Chongqing 400715 Peoples R China Xi An Jiao Tong Univ Sch Math & Stat Xian 710049 Peoples R China Ningxia Univ Sch Informat Engn Yinchuan 750021 Peoples R China Southwest Univ Sch Math & Stat Chongqing 400715 Peoples R China Southwest Univ Res Inst Intelligent Finance & Digital Econ Chongqing 400715 Peoples R China Texas A&M Univ Qatar Dept Math Doha Qatar

Within the tensor singular value decomposition (T-SVD) framework, existing robust low-rank tensor completion approaches have made great achievements in various areas of science and engineering. Nevertheless, these methods involve the T-SVD based low-rank approximation, which suffers from high computational costs when dealing with large-scale tensor data. Moreover, most of them are only applicable to third-order tensors. Against these issues, in this article, two efficient low-rank tensor approximation approaches fusing random projection techniques are first devised under the order-d ( d >= 3 ) T-SVD framework. Theoretical results on error bounds for the proposed randomized algorithms are provided. On this basis, we then further investigate the robust high-order tensor completion problem, in which a double nonconvex model along with its corresponding fast optimization algorithms with convergence guarantees are developed. Experimental results on large-scale synthetic and real tensor data illustrate that the proposed method outperforms other state-of-the-art approaches in terms of both computational efficiency and estimated precision.

关键词： High-order T-SVD framework robust high-order tensor completion randomized low-rank tensor approximation nonconvex regularizers admm algorithm

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