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Dynamic mobility and handover management in software-defined networking-based fifth-generation heterogeneous networks

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INTERNATIONAL JOURNAL OF NETWORK MANAGEMENT 2025年第1期35卷

作者： Khan, Adil Ahmad, Shabeer Ali, Ihsan Hayat, Babar Tian, Yanan Liu, Weixing Xian Eurasia Univ Sch Informat Engn Xian Peoples R China Beijing Univ Posts & Telecommun Sch Elect Engn Beijing Peoples R China Southeast Missouri State Univ Dept Comp Sci Cape Girardeau MO USA

The adoption and application of mobile communication technology have rapidly escalated, leading to a significant upsurge in the demand of data traffic. Ultra-densification stands as one of the network solutions within the realm of 5G and beyond technologies, aimed to enhance data rates and network capacity. Heterogeneous networks (HetNets) are deployed with different types of small cells (SCs) in mobile networks to provide high capacity, data rate, throughput, and low latency communication. HetNet solves the problem of network densification at the expense of mobility management problems such as ping-pong handover, unnecessary handovers, handover delay, and cell load. This paper introduces an enhanced optimal cell selection technique employing software-defined networking (SDN) to tackle the challenges of handover and mobility management in 5G and beyond 5G (B5G) HetNet. The proposed SDN-based cell selection scheme leverages linear programming (LP) to manage the mobility of users dynamically, facilitating the selection of the optimal cell for user equipment (UE) handover. This selection is based on multi-attribute decision-making criteria, which include user direction, received signal strength (RSS) value, cell load, and dwell time. By applying LP, computational overhead during cell selection is significantly reduced. The results indicate that the proposed scheme leads to a 39% reduction in number of handovers. This reduction signifies a substantial advancement in mitigating issues associated with frequent and unnecessary handovers, ultimately leading to minimized signaling overhead between UE and cells. Moreover, the proposed solution outperformed the existing scheme in terms of system's throughput and selects an optimal target cell with a lower cell load. This article proposes an optimal next cell selection technique for users in the software-defined heterogeneous network to tackle the challenges of handover and mobility management. The technique uses linear program

关键词： handover HetNets linear programming mobility management multi-attribute decision making SDN

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How to Measure the Researcher Impact with the Aid of its Impactable Area: A Concrete Approach Using Distance Geometry

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JOURNAL OF CLASSIFICATION 2025年第1期42卷 253-281页

作者： Cappelletti-Montano, Beniamino Cherchi, Gianmarco Manca, Benedetto Montaldo, Stefano Musio, Monica Univ Cagliari Dept Math & Comp Sci Via Osped 72 I-09124 Cagliari Italy

Assuming that the subject of each scientific publication can be identified by one or more classification entities, we address the problem of determining a similarity function (distance) between classification entities based on how often two classification entities are used in the same publication. This similarity function is then used to obtain a representation of the classification entities as points of an Euclidean space of a suitable dimension by means of optimization and dimensionality reduction algorithms. This procedure allows us also to represent the researchers as points in the same Euclidean space and to determine the distance between researchers according to their scientific production. As a case study, we consider as classification entities the codes of the American Mathematical Society Classification System.

关键词： Bibliometrics Dimensionality reduction linear programming

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Distribution network optimization: predicting computation times to design scenario analysis for network operators

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ENERGY SYSTEMS-OPTIMIZATION MODELING SIMULATION AND ECONOMIC ASPECTS 2025年第1期16卷 227-254页

作者： Burmeister, Sascha Christian Schryen, Guido Paderborn Univ Warburger Str 100 D-33098 Paderborn NRW Germany

The increased use of renewable energies promotes decarbonization and raises the load on power distribution networks, forcing responsible distribution network operators to re-evaluate and re-design their networks. Infrastructure planners employ a rolling-horizon planning procedure with frequent recalculations to face informational uncertainty, which require solving multiple scenarios. Keeping complexity manageable is particularly challenging as distribution network areas may span multiple cities and counties. In this study, we focus on infrastructural decomposition, where the distribution network is decomposed into multiple parts and planning problems, which are then optimized separately. However, infrastructure planners lack the knowledge of how they should design a scenario analysis for a subnetwork to account for informational uncertainties subject to limited planning time and computing resources. Based on empirical requirements from literature and discussions with experts, we present a novel mixed integer linear optimization model that allows to use exact solution approaches for realistic large-scale distribution networks. Our approach considers the primary and secondary distribution network in an integrated way and designs a flexible topology for high reliability power distribution. We perform extensive computational experiments and a sensitivity analysis to determine correlations between the values of model parameters and computation times required to solve the resulting model instances to optimality. The results of the sensitivity analysis indicate that the combination of the number of buses, lines and the considered action scope have a considerable influence on the solving time. In contrast, a higher number of available transformers led to a better solvability of the model. From these computational insights, we derive implications for infrastructure planners who wish to perform scenario analysis for planning their power distribution networks.

关键词： OR in energy Networks Distribution network optimization linear programming

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Exact Recovery for System Identification With More Corrupt Data Than Clean Data

IEEE OPEN JOURNAL OF CONTROL SYSTEMS

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IEEE OPEN JOURNAL OF CONTROL SYSTEMS 2025年 4卷 1-17页

作者： Yalcin, Baturalp Zhang, Haixiang Lavaei, Javad Arcak, Murat Univ Calif Berkeley Dept Ind Engn & Operat Res Berkeley CA 94720 USA Univ Calif Berkeley Dept Math Berkeley CA 94720 USA Univ Calif Berkeley Dept Elect Engn & Comp Sci Berkeley CA 94720 USA

This paper investigates the system identification problem for linear discrete-time systems under adversaries and analyzes two lasso-type estimators. We examine non-asymptotic properties of these estimators in two separate scenarios, corresponding to deterministic and stochastic models for the attack times. We prove that when the system is stable and attacks are injected periodically, the sample complexity for exact recovery of the system dynamics is linear in terms of the dimension of the states. When adversarial attacks occur at each time instance with probability p, the required sample complexity for exact recovery scales polynomially in the dimension of the states and the probability p. This result implies almost sure convergence to the true system dynamics under the asymptotic regime. As a by-product, our estimators still learn the system correctly even when more than half of the data is compromised. We emphasize that the attack vectors are allowed to be correlated with each other in this work. This paper provides the first mathematical guarantee in the literature on learning from correlated data for dynamical systems in the case when there is less clean data than corrupt data.

关键词： Vectors Noise System identification Complexity theory Control systems linear systems Dynamical systems System dynamics linear programming Trajectory robust control statistical learning system identification

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Selection of waste treatment methods for food sources: an integrated decision model using q-rung fuzzy data, LOPCOW, and COPRAS techniques

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Clean Technologies and Environmental Policy 2025年 1-25页

作者： Dhruva, Sundararajan Krishankumar, Raghunathan Ravichandran, Kattur Soundarapandian Kaklauskas, Arturas Zavadskas, Edmundas Kazimieras Gupta, Pankaj Department of Mathematics Amrita School of Physical Sciences Amrita Vishwa Vidyapeetham Coimbatore641112 India Information Technology Systems and Analytics Area Indian Institute of Management Bodh Gaya Bodh Gaya824234 India Laboratory of Smart Building Systems Institute of Sustainable Construction Faculty of Civil Engineering Vilnius Gediminas Technical University Vilnius10223 Lithuania Department of Construction Management and Real Estate Vilnius Gediminas Technical University Vilnius10223 Lithuania Institute of Sustainable Construction Vilnius Gediminas Technical University Vilnius10223 Lithuania Department of Operational Research University of Delhi Delhi110007 India

Waste treatment transforms waste into valuable resources, addressing environmental challenges while supporting sustainable practices in energy recovery, resource management, and pollution control. This study addresses the selection of appropriate "Food Waste Treatment Method" (FWTM), an important component in sustainability that requires effective management. The existing models of FWTM selection have some limitations that include (i) inadequate handling of uncertainty, (ii) insufficient systemic determination of experts’ importance, and (iii) lack of customized ranking based on user preferences. To address these gaps in selecting FWTM, this study proposes an integrated and personalized "Multi-Criteria Decision-Making" (MCDM) framework. In this framework, "q-Rung Orthopair Fuzzy Set" (qROFS) has been utilized to manage data uncertainty since it presents the ratings of FWTM based on various criteria as a tuple containing a degree of preference and non-preference. To systematically determine the expert weights and criteria weights, "linear programming" (LP) and "LOgarithmic Percentage Change-driven Objective Weighting" (LOPCOW) have been used, respectively. A novel extension of "COmplex PRopotional ASsessment" (COPRAS), a query-based COPRAS, has been used in this framework. This extension of COPRAS offers adaptability and personalization in the selection of FWTM since it also considers the user’s preference for the kind of FWTM based on the user’s requirements and specifications. A case study is also presented, which helps understand the model’s applicability. Notably, from the selected FWTMs, the model identified anaerobic digestion as an optimal FWTM, followed by incineration and heat-moisture reaction. Sensitivity and comparative analyses are performed to understand the strengths and weaknesses of the model. Results from the sensitivity analysis of queries infer that there is a strong effect of query vector(s) on the ranking of FWTMs. As the number of queries incre

关键词： linear programming

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Importance Inference of Optimal Test Planning for Degradation Analysis

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IEEE TRANSACTIONS ON RELIABILITY 2025年

作者： Dong, Yi-Shian Peng, Chien-Yu Natl Chengchi Univ Dept Stat Taipei 11605 Taiwan Acad Sinica Inst Stat Sci Taipei 11529 Taiwan

Determination of the decision variables such as the inspection period, number of measurements, and sample size is crucial for planning an efficient degradation test. For widely used stochastic processes, the necessary and sufficient conditions for the explicit expression of optimal decision variables can be derived by minimizing the approximate variance of an estimator of interest under a limited budget. The importance of the decision variable is proposed to study the rate at which the objective function improves with the decision variable. The necessary and sufficient conditions for determining the importance of the optimal decision variables are theoretically investigated to elucidate the effect of the experimental costs and model parameters. Furthermore, the relative rankings of the importance of the optimal decision variables are illustrated through numerical examples.

关键词： Costs Degradation linear programming Sufficient conditions Numerical models Planning Size measurement Minimization Maximum likelihood estimation Vectors Constrained optimization interaction sensitivity analysis step-stress accelerated degradation test Tweedie process

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A local convergent ecological inference algorithm for RxC tables

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JOURNAL OF MATHEMATICAL SOCIOLOGY 2025年第1期49卷 25-46页

作者： Pavia, Jose M. Univ Valencia Dept Appl Econ GIPEyOP Valencia 46022 Spain

Over the years, a number of methods have been proposed to forecast the unknown inner-cell values of a set of related RxC contingency tables when only their margins are known. This is a classical problem that emerges in many areas, from economics to quantitative history, being particularly ubiquitous when dealing with electoral data in sociology and political science. However, the two current major algorithms to solve this problem, based on Bayesian statistics and iterative linear programming depend on adjustable (hyper-)parameters and do not yield a unique solution: their estimates tend to fluctuate (when convergence is reached) around a stationary distribution. Within the linear programming framework, this paper proposes a new algorithm (lclphom) that always converges to a unique solution, having no adjustable parameters. This characteristic makes it easy to use and robust to claims of hacking. Furthermore, after assessing lclphom with real and simulated data, lclphom is found to yield estimates of (almost) similar accuracy to the current major solutions, being more preferable to the other lphom-family algorithms the more heterogeneous the row-fraction distributions of the tables are. Interested practitioners can easily use this new algorithm as it has been programmed in the R-package lphom.

关键词： Contingency tables elections linear programming lphom vote transitions

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MILP Acceleration: A Survey from Perspectives of Simplex Initialization and Learning-Based Branch and Bound

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JOURNAL OF THE OPERATIONS RESEARCH SOCIETY OF CHINA 2025年第1期13卷 1-55页

作者： Huang, Meng-Yu Huang, Ling-Ying Zhong, Yu-Xing Yang, Hui-Wen Chen, Xiao-Meng Huo, Wei Wang, Jia-Zheng Zhang, Fan Bai, Bo Shi, Ling Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China Huawei Hong Kong Res Ctr Theory Lab Hong Kong Peoples R China

Mixed integer linear programming (MILP) is an NP-hard problem, which can be solved by the branch and bound algorithm by dividing the original problem into several subproblems and forming a search tree. For each subproblem, linear programming (LP) relaxation can be solved to find the bound for making the following decisions. Recently, with the increasing dimension of MILPs in different applications, how to accelerate the solution process becomes a huge challenge. In this survey, we summarize techniques and trends to speed up MILP solving from two perspectives. First, we present different approaches in simplex initialization, which can help to accelerate the solution of LP relaxation for each subproblem. Second, we introduce the learning-based technologies in branch and bound algorithms to improve decision making in tree search. We also propose several potential directions and extensions to further enhance the efficiency of solving different MILP problems.

关键词： MILP acceleration Simplex initialization linear programming Mixed integer linear programming Machine learning

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Differentially private histogram with valid statistics

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STATISTICS & PROBABILITY LETTERS 2025年 219卷

作者： Cao, Zilong Wu, Shisong Li, Xuanang Zhang, Hai Northwest Univ Sch Math Xian 710127 Peoples R China China Southern Power Grid Artificial Intelligence Guangzhou Peoples R China

Differentially private histograms (DP-Histograms) are integral to data publication and privacy preservation efforts. However, conventional DP-Histograms often fail to preserve valid statistical information and the essential characteristics of the original data. This paper shows the invalidity of variance is the inherent shortcomings in general DP-Histograms, and introduces a novel algorithm called the Differentially Private Histogram with Valid Statistics (VSDPH) to overcome the problem. The VSDPH, grounded in linear programming and bounded Lipschitz distance, efficiently generates DP histograms while preserving the valid statistics of the original data. Our theoretical analysis demonstrates that histograms produced by VSDPH maintain asymptotically valid variance, and we establish an upper bound based on the 1-Wasserstein distance. Through experiments, we validate that VSDPH can accurately hold the statistical characteristics of the original data. This capability brings the resulting histograms closer to the originals.

关键词： Differential privacy Histogram Wasserstein distance Statistics linear programming

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Collision Avoidance Based on Stochastic Model Predictive Control in Collaboration Between ROV and AUV

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2025年

作者： Cao, Xiang Wang, Xuerao Sun, Changyin Anhui Univ Sch Artificial Intelligence Hefei 230601 Peoples R China Minist Educ Engn Res Ctr Autonomous Unmanned Syst Technol Hefei 230601 Peoples R China Anhui Prov Key Lab Secur Artificial Intelligence Hefei 230601 Peoples R China Southeast Univ Sch Automat Nanjing 210096 Peoples R China

Due to certain technical limitations of autonomous underwater vehicle (AUV), they cannot completely perform complex tasks independently. When performing complex tasks, coordination between the remote operated vehicle (ROV) and AUV is required. Therefore, collision avoidance is a key technology to ensure vehicle safety. During collision avoidance, AUV need to understand human intentions, make decisions, and perform the corresponding actions. To solve the problems of human intention uncertainty and random noise interference, an AUV collision avoidance strategy based on a dynamic Bayesian network and stochastic model predictive control (SMPC) is proposed in this paper. First, a dynamic Bayesian network is used to assess the probability of AUV collisions in the system. Then, using the properties of Gaussian distribution and related theorems, the objective function is simplified and transformed into a deterministic model predictive control problem. Finally, the intention-exploration item is added to the objective function to better understand human intention. Through the simulations and experiments in specific scenarios, it is verified that the proposed collision avoidance control strategy can safely and effectively control a hybrid system with the coexistence of ROV and AUV.

关键词： Collision avoidance Vehicle dynamics Bayes methods Artificial intelligence Robots linear programming Autonomous underwater vehicles Trajectory Predictive control Noise Collision avoidance strategy AUV human intention dynamic Bayesian network stochastic model predictive control

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