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Complexity Optimized Digital Predistortion Model of RF Power Amplifiers

IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES 2022年第3期70卷 1490-1499页

作者： Cao, Wenhui Wang, Siqi Landin, Per N. Fager, Christian Eriksson, Thomas Chalmers Univ Technol Dept Elect Engn SE-41296 Gothenburg Sweden Ericsson AB SE-16440 Kista Sweden Chalmers Univ Technolo Dept Microtechnol & Nanosci MC2 SE-41296 Gothenburg Sweden

A novel behavioral modeling technique for digital predistortion of radio frequency power amplifiers (PAs) is proposed. The proposed basis-propagating selection (BAPS) model has an advantage on complexity since its basis functions are constructed as delay or a combination of existing basis functions. The BAPS model effectively optimizes the running time complexity while maintaining the generality of a full Volterra series. A greedy search procedure is applied to select the optimal basis functions. Compared with truncated Volterra models and pruned models obtained by popular pruning techniques, the superior performance of the BAPS model, in terms of accuracy and identification stability, has been fairly justified. The linearization measurements on different types of PAs illustrate that the BAPS model can robustly outperform other models from literature while achieving a low running complexity.

关键词： Complexity theory Adaptation models Predistortion Predictive models Delays Radio frequency Costs Basis-propagating selection (BAPS) digital predistortion (DPD) greedy algorithm low-cost complexity modeling and or linearization techniques power amplifiers (PA)

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Improved algorithms for non-submodular function maximization problem

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THEORETICAL COMPUTER SCIENCE 2022年 931卷 49-55页

作者： Liu, Zhicheng Jin, Jing Chang, Hong Du, Donglei Zhang, Xiaoyan Beijing Univ Technol Beijing Inst Sci & Engn Comp Beijing 100124 Peoples R China Nanjing Normal Univ Coll Taizhou Tiazhou 225300 Peoples R China Nanjing Normal Univ Inst Math Sch Math Sci Nanjing 210023 Peoples R China Univ New Brunswick Fac Management Fredericton NB E3B5A3 Canada

The concept of submodularity finds wide applications in data science, artificial intelligence, and machine learning, providing a boost to the investigation of new ideas, innovative techniques, and creative algorithms to solve different submodular optimization problems arising from a diversity of applications. However pure submodular or supermodular problems only represent a small portion of the problems we are facing in real life applications. The main focus of this work is to consider a non-submodular function maximization problem subject to a cardinality constraint, where the objective function is the sum of a monotone gamma-weakly submodular function and a supermodular function. This problem includes some previously studied problems as special cases, such as the submodular+supermodular maximization problem when gamma =1, and the gamma-weakly submodular function maximization problem when the supermodular function is void. We present greedy algorithms for this generalized problem under both offline and streaming models, improving existing results.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Non-submodular function maximization greedy algorithm Cardinality constraint Offline model Streaming model

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A solution for the greedy approximation of a step function with a waveform dictionary

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COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION 2023年 116卷

作者： Rivero, Jorge Andres Vellucci, Pierluigi Univ Washington Deptartment Econ Savery Hall305 Spokane LnBox 353330 Seattle WA 98105 USA Roma Tre Univ Dept Econ Via Silvio Am 77 I-00145 Rome Italy

In this paper we consider a step function characterized by an arbitrary sequence of real -valued scalars and approximate it with a matching pursuit (MP) algorithm. We utilize a waveform dictionary with rectangular window functions as part of this algorithm. We show that the waveform dictionary is not necessary when all of the scalars are either non positive or non negative and the parameters of a wavelet dictionary on an integer lattice yields a closed-form solution for the initial optimization problem as part of the MP. Additionally, for any real-valued scalar sequence, we provide a solution with a related wavelet dictionary at each iteration of the algorithm. This allows for practical calculation of the approximating function, which we use to provide examples on simulated and real univariate time series data that display discontinuities in its underlying structure where the step function can be thought of as a sample from a signal of interest.(c) 2022 Elsevier B.V. All rights reserved.

关键词： greedy algorithm Hilbert space Wavelet Step function Projection pursuit Time series Data

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Optimal planning of indoor laser scans based on continuous optimization

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AUTOMATION IN CONSTRUCTION 2022年 143卷

作者： Zeng, Yan Liu, Jiepeng Cao, Qian Wu, Zhou Chen, Bo Li, Dongsheng Cheng, Guozhong Chongqing Univ Sch Civil Engn Chongqing 400045 Peoples R China Chengdu Construct Engn Grp Co Ltd Chengdu 610000 Sichuan Peoples R China Shenzhen Univ Coll Civil & Transportat Engn Shenzhen 518060 Guangdong Peoples R China Chongqing Univ Sch Automat Chongqing 400045 Peoples R China

The automatic scanning system of terrestrial laser scanning requires an efficient scanning solution owing to the time-consuming and labor-intensive data acquisition process. Most existing methods tend to achieve a prior scan planning by using a discrete solving process but need a user-defined discretization resolution, which often leads to the challenge of finding the scanning locations. This study describes a two-dimensional (2D) based scheme for scan planning. In the proposed method, the scanning location consensus based on multi-objective optimization is designed to guarantee the registrability and completeness of scan planning. A breadth-first strategy combined with a backtracking process is employed to find a minimal scanning location set. Validation experiments have been conducted on two real-world scenes and verified the effectiveness and feasibility of the proposed method. The proposed method achieves the optimal scan planning and yields almost 90%, 70% improvement on planning time compared with greedy approach and expert plans separately.

关键词： Planning for scanning Multi -objective Heuristic optimization greedy algorithm Terrestrial laser scanning

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The sum of root-leaf distance interdiction problem by upgrading edges/nodes on trees

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JOURNAL OF COMBINATORIAL OPTIMIZATION 2022年第1期44卷 74-93页

作者： Zhang, Qiao Guan, Xiucui Jia, Junhua Qian, Xinqiang Southeast Univ Sch Math Nanjing 210096 Peoples R China

Network interdiction problems by upgading critical edges/nodes have important applications to reduce the infectivity of the COVID-19. A network of confirmed cases can be described as a rooted tree that has a weight of infectious intensity for each edge. Upgrading edges (nodes) can reduce the infectious intensity with contacts by taking prevention measures such as disinfection (treating the confirmed cases, isolating their close contacts or vaccinating the uninfected people). We take the sum of root-leaf distance on a rooted tree as the whole infectious intensity of the tree. Hence, we consider the sum of root-leaf distance interdiction problem by upgrading edges/nodes on trees (SDIPT-UE/N). The problem (SDIPT-UE) aims to minimize the sum of root-leaf distance by reducing the weights of some critical edges such that the upgrade cost under some measurement is upper-bounded by a given value. Different from the problem (SDIPT-UE), the problem (SDIPT-UN) aims to upgrade a set of critical nodes to reduce the weights of the edges adjacent to the nodes. The relevant minimum cost problem (MCSDIPT-UE/N) aims to minimize the upgrade cost on the premise that the sum of root-leaf distance is upper-bounded by a given value. We develop different norms to measure the upgrade cost. Under weighted Hamming distance, we show the problems (SDIPT-UE/N) and (MCSDIPT-UE/N) are NP-hard by showing the equivalence of the two problems and the 0-1 knapsack problem. Under weighted l1 norm, we solve the problems (SDIPT-UE) and (MCSDIPT-UE) in O(n) time by transforimg them into continuous knapsack problems. We propose two linear time greedy algorithms to solve the problem (SDIPT-UE) under unit Hamming distance and the problem (SDIPT-UN) with unit cost, respectively. Furthermore, for the the minimum cost problem (MCSDIPT-UE) under unit Hamming distance and the problem (MCSDIPT-UN) with unit cost, we provide two O(n log n) time algorithms by the binary search methods. Finally, we perform some numeri

关键词： Network interdiction problem Upgrading critical edges Upgrading critical nodes Tree Knapsack problem greedy algorithm

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Model Order Reduction for Delayed PEEC Models With Guaranteed Accuracy and Observed Stability

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS 2022年第10期69卷 4177-4190页

作者： Feng, Lihong Lombardi, Luigi Benner, Peter Romano, Daniele Antonini, Giulio Max Planck Inst Dynam Complex Tech Syst D-39106 Magdeburg Germany Micron Semicond I-67051 Avezzano Italy Univ Aquila Dept Ind & Informat Engn & Econ UAq EMC Lab I-67100 Laquila Italy

In this work, a stable and accurate model order reduction method for large-scale partial element equivalent circuit (PEEC) models with many delays is proposed. The method reduces the dimension of the original model by interpolating the original transfer function. The interpolation points are iteratively selected using a greedy algorithm. An efficient error estimator for the reduced transfer function makes the greedy algorithm very successful in both, properly selecting the interpolation points and tightly measuring the error of the reduced transfer function. The resulting reduced-order model is accurate both in the frequency and in the time domain. Numerical tests have shown that the reduced-order model successfully filters the instability behavior of the original model and exhibits stability over a large time interval.

关键词： Transfer functions Read only memory Time-domain analysis Integrated circuit modeling Delays Interpolation greedy algorithms Partial element equivalent circuit (PEEC) method model order reduction greedy algorithm error estimation

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Joint computation offloading and deployment optimization in multi-UAV-enabled MEC systems

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PEER-TO-PEER NETWORKING AND APPLICATIONS 2022年第1期15卷 194-205页

作者： Chen, Zheyi Zheng, Hongqiang Zhang, Jianshan Zheng, Xianghan Rong, Chunming Univ Exeter Dept Comp Sci Coll Engn Math & Phys Sci Exeter EX4 4QF Devon England Fuzhou Univ Coll Math & Comp Sci Fuzhou 350116 Peoples R China Univ Stavanger Dept Elect Engn & Comp Sci N-4036 Stavanger Norway

The combination of unmanned aerial vehicles (UAVs) and mobile edge computing (MEC) technology breaks through the limitations of traditional terrestrial communications. The effective line-of-sight channel provided by UAVs can greatly improve the communication quality between edge servers and mobile devices (MDs). To further enhance the Quality-of-Service (QoS) of MEC systems, a multi-UAV-enabled MEC system model is designed. In the proposed model, UAVs are regarded as edge servers to offer computing services for MDs, aiming to minimize the average task response time by jointly optimizing UAV deployment and computation offloading. Based on the problem definition, a two-layer joint optimization method (PSO-GA-G) is proposed. First, the outer layer utilizes a Particle Swarm Optimization algorithm combined with Genetic algorithm operators (PSO-GA) to optimize UAV deployment. Next, the inner layer adopts a greedy algorithm to optimize computation offloading. The extensive simulation experiments verify the feasibility and effectiveness of the proposed PSO-GA-G. The results show that the PSO-GA-G can achieve a lower average task response time than the other three baselines.

关键词： Mobile edge computing Unmanned aerial vehicle deployment Computation offloading Particle swarm optimization Genetic algorithm greedy algorithm

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Automatic Pricing and Replenishment Decision for Vegetable Products Based on ARIMA Model and Intelligent algorithm 3

Automatic Pricing and Replenishment Decision for Vegetable P...

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3rd IEEE International Conference on Electrical Engineering, Big Data and algorithms, EEBDA 2024

作者： Zhang, Zixun University of Shanghai for Science and Technology Shanghai China

ISBN: (纸本)9798350380989

With the improvement of human requirements for quality of life, economic development and urbanization, the automatic pricing and replenishment decisions of vegetable categories and single products in supermarkets have more reasonable planning. To optimize the profitability of supermarkets, help them expand their market share, and determine their market position, this paper aims to explore the correlation between vegetable categories and single products, and to set a more reasonable replenishment and pricing strategy for supermarkets. Firstly, according to the distribution law and interrelationship of the sales volume of various categories and single products of vegetables, the sales volume of each category and single product was quantitatively analyzed, and because there was no normal distribution relationship in the data, the Spearman correlation mathematical model was established to obtain the monotonic relationship between vegetable categories. Then, the relationship between the total sales volume of each vegetable category and the cost-plus pricing was analyzed, and the dimensionality reduction of the data was carried out by using the calculation data to determine whether it conformed to the multivariate linear relationship, and the discount situation was added as a special for observation and fitting, and the total sales volume and the cost-plus pricing were inversely proportional. Finally, by optimizing the total number of single products sold, the minimum display quantity, and the single product replenishment number of saleable varieties, the existing conditions are fixed and compressed, and the greedy algorithm is modified to obtain a new supermarket revenue and single product replenishment planning model, and the optimal replenishment strategy and pricing method are obtained by solving the model. © 2024 IEEE.

关键词： greedy algorithm Spearman Model Supermarket Revenue and Single Product Replenishment Planning Model Time Series

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Model averaging with privacy-preserving

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COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2022年第4期51卷 1401-1414页

作者： He, Baihua Dong, Fangli Wuhan Univ Sch Math & Stat Wuhan 430072 Hubei Peoples R China Shanghai Jiao Tong Univ Sch Math Sci Shanghai Peoples R China

Model averaging approach is an effective technique in alleviating the risk of misspecification and improving prediction accuracy for datasets with high dimension. When the datasets consist of sensitive information, it is nature to extend traditional model averaging approach to protect individual privacy while permitting utility. Specifically, we provide a modified Frank-Wolfe algorithm, which applies the objective perturbation in each iteration of finding the optimal descent directions to attain the model weight. It is easy to implement and feasible. Furthermore, we theoretically prove the new algorithm can achieve privacy and utility guarantee. Extensive experiments on both simulated and real world datasets are carried out to back up the theory, demonstrating the encouraging performances of the proposed method in managing the inherent tradeoff between privacy and accuracy.

关键词： Privacy Model aggregation Objective perturbation greedy algorithm High dimension Prediction

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A Reduced Basis Method for Radiative Transfer Equation

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JOURNAL OF SCIENTIFIC COMPUTING 2022年第1期91卷 1-27页

作者： Peng, Zhichao Chen, Yanlai Cheng, Yingda Li, Fengyan Michigan State Univ Dept Math E Lansing MI 48824 USA Univ Massachusetts Dept Math 285 Old Westport Rd N Dartmouth MA 02747 USA Michigan State Univ Dept Math Dept Computat Math Sci & Engn E Lansing MI 48824 USA Rensselaer Polytech Inst Dept Math Sci Troy NY 12180 USA

Linear kinetic transport equations play a critical role in optical tomography, radiative transfer and neutron transport. The fundamental difficulty hampering their efficient and accurate numerical resolution lies in the high dimensionality of the physical and velocity/angular variables and the fact that the problem is multiscale in nature. Leveraging the existence of a hidden low-rank structure hinted by the diffusive limit, in this work, we design and test the angular-space reduced order model for the linear radiative transfer equation, the first such effort based on the celebrated reduced basis method (RBM). Our method is built upon a high-fidelity solver employing the discrete ordinates method in the angular space, an asymptotic preserving upwind discontinuous Galerkin method for the physical space, and an efficient synthetic accelerated source iteration for the resulting linear system. Addressing the challenge of the parameter values (or angular directions) being coupled through an integration operator, the first novel ingredient of our method is an iterative procedure where the macroscopic density is constructed from the RBM snapshots, treated explicitly and allowing a transport sweep, and then updated afterwards. A greedy algorithm can then proceed to adaptively select the representative samples in the angular space and form a surrogate solution space. The second novelty is a least squares density reconstruction strategy, at each of the relevant physical locations, enabling the robust and accurate integration over an arbitrarily unstructured set of angular samples toward the macroscopic density. Numerical experiments indicate that our method is effective for computational cost reduction in a variety of regimes.

关键词： kinetic reduced order model reduced basis multiscale greedy algorithm least squares asymptotic preserving

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