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Multi-objective network resource allocation method based on fractional PID control

SIGNAL PROCESSING 2025年 227卷

作者： Ni, Xintong Wei, Yiheng Zhou, Shuaiyu Tao, Meng Southeast Univ Sch Math Nanjing 211189 Peoples R China

In this paper, a fractional proportional-integral-derivative (PID) distributed optimization algorithm is proposed to solve the network resource allocation problem. The algorithm combines fractional calculus and the concept of PID control, which improves the convergence rate and increases the freedom, flexibility and potential with multiple parameters compared with the existing algorithms. Meanwhile, the results of simulation study verified the efficiency and superiority of the algorithm.

关键词： Multi-objective optimization Resource allocation distributed algorithms PID Fractional calculus

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Quantized Privacy-Preserving algorithms for Homogeneous and Heterogeneous Networks With Finite Transmission Guarantees

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IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2025年第1期12卷 325-337页

作者： Rikos, Apostolos I. Hadjicostis, Christoforos N. Johansson, Karl H. Hong Kong Univ Sci & Technol Guangzhou Artificial Intelligence Thrust Informat Hub Guangzhou 511453 Peoples R China Hong Kong Univ Sci & Technol Dept Comp Sci & Engn Clear Water Bay Hong Kong Peoples R China Univ Cyprus Dept Elect & Comp Engn CY-1678 Nicosia Cyprus KTH Royal Inst Technol Div Decis & Control Syst SE-10044 Stockholm Sweden Digital Futures SE-10044 Stockholm Sweden

Privacy protection is increasingly critical in various applications due to the prevalence of networked systems. In this article, we focus on preserving the privacy of nodes' initial states while computing their average in a network. Curious nodes attempt to identify the initial states of other nodes without interfering in the computation. To address this challenge, we propose two privacy-preserving algorithms. The first algorithm operates over homogeneous networks, i.e., nodes have consistent processing delays and are able to communicate in a synchronous fashion. The second algorithm operates over heterogeneous networks, i.e., nodes have varying processing delays and communicate in an asynchronous fashion. Our algorithms exhibit efficient communication, finite-time convergence, and operation termination after convergence, making them suitable for resource-constrained environments. We also present topological conditions under which our algorithms enable privacy preservation. Finally, we apply our algorithms to a smart grid system and compare their performance against other algorithms, emphasizing their advantages in communication efficiency, convergence rate, and privacy preservation.

关键词： Privacy Convergence Smart grids Control systems Network systems Heterogeneous networks Delays distributed algorithms finite-time convergence finite transmission privacy-preserving average consensus quantized communication smart grid

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Move-optimal arbitrary pattern formation by mobile robots on rectangular grid using near-optimal spatial area

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THEORETICAL COMPUTER SCIENCE 2025年 1038卷

作者： Sharma, Avisek Ghosh, Satakshi Goswami, Pritam Sau, Buddhadeb Jadavpur Univ Dept Math Kolkata 700032 West Bengal India Int Inst Informat Technol IIIT Bhubaneswar Dept Basic Sci & Humanities Bhubaneswar 751003 Odisha India Sister Nivedita Univ Dept Comp Sci & Engn Kolkata 700156 West Bengal India

Arbitrary pattern formation (APF) is a well-studied problem in swarm robotics. To the best of our knowledge, the problem has been considered in two different settings: one in a euclidean plane and another in an infinite grid. This work deals with the problem in an infinite rectangular grid setting. The previous works in literature dealing with the APF problem in an infinite grid had a fundamental issue. These deterministic algorithms use a lot of spatial area in the grid to solve the problem, mainly to maintain the asymmetry of the configuration and avoid any collision. These solution techniques cannot be useful if there is a spatial constraint in the application field. In this work, we consider luminous robots (each robot equipped with a light that can take three colors) to avoid symmetry, but we carefully designed a deterministic algorithm that solves the APF problem using the minimal required spatial area in the grid if the initial pattern is asymmetric. The robots are autonomous, identical, and anonymous, and they operate in Look-Compute-Move cycles under a fully-asynchronous scheduler. The APF algorithm proposed in [1] by Bose et al. can be modified using luminous robots so that it uses minimal spatial area, but that algorithm is not move-optimal. The algorithm proposed in this paper not only uses minimal spatial area but is also asymptotically move-optimal.

关键词： distributed algorithms Arbitrary pattern formation Rectangular grid Robot with lights Optimal algorithms

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Maximum independent set formation on a finite grid by myopic robots

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THEORETICAL COMPUTER SCIENCE 2025年 1031卷

作者： Das, Raja Sharma, Avisek Sau, Buddhadeb Jadavpur Univ Dept Math Kolkata India

This work deals with the Maximum Independent Set (MAXIS) formation problem in a finite rectangular grid by autonomous robots. Suppose we are given a set of identical robots, where each robot is placed on a node of a finite rectangular grid G such that no two robots are on the same node. The MAXIS formation problem asks to design an algorithm and each robot will move autonomously after executing the algorithm and terminate at a node such that after a finite time the set of nodes occupied by the robots is a maximum independent set of G. We assume that robots are anonymous and they execute the same distributed algorithm. Previous works solved this problem using one or several door nodes through which the robots enter the grid or the graph one by one and occupy the required nodes. In this work, we propose a deterministic algorithm that solves the MAXIS formation problem in a more generalized scenario, i.e., when the total number of required robots to form a MAXIS are arbitrarily placed on the grid. The proposed algorithm works under a semi-synchronous scheduler using robots with only two hop visibility range and only three colors.

关键词： Myopic robot Maximum independent set Finite grid Autonomous robots Robot with lights distributed algorithms

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A Safe First-Order Method for Pricing-Based Resource Allocation in Safety-Critical Networks

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IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2025年第1期12卷 521-533页

作者： Turan, Berkay Hutchinson, Spencer Alizadeh, Mahnoosh Univ Calif Santa Barbara Dept Elect & Comp Engn Santa Barbara CA 93106 USA

We introduce a novel algorithm for solving network utility maximization (NUM) problems that arise in resource allocation schemes over networks with known safety-critical constraints, where the constraints form an arbitrary convex and compact feasible set. Inspired by applications where customers' demand can only be affected through posted prices and real-time two-way communication with customers is not available, we require an algorithm to generate "safe prices." This means that at no iteration should the realized demand in response to the posted prices violate the safety constraints of the network. Thus, in contrast to existing distributed first-order methods, our algorithm, called safe pricing for NUM (SPNUM), is guaranteed to produce feasible primal iterates at all iterations. At the heart of the algorithm lie two key steps that must go hand in hand to guarantee safety and convergence: first, applying a projected gradient method on a shrunk feasible set to get the desired demand, and second, estimating the price response function of the users and determining the price so that the induced demand is close to the desired demand. We ensure safety by adjusting the shrinkage to account for the error between the induced demand and the desired demand. In addition, by gradually reducing the amount of shrinkage and the step size of the gradient method, we prove that the primal iterates produced by the SPNUM achieve a sublinear static regret of O ( log (T)) after T time steps.

关键词： Resource management Pricing Gradient methods Optimization Safety Vectors Iterative algorithms Convergence Network systems Convex functions distributed algorithms multi-agent systems optimization methods safety smart devices

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Decentralized management of unbalanced multi-agent distribution systems: A transactive approach leveraging ADMM framework

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IET GENERATION TRANSMISSION & DISTRIBUTION 2025年第1期19卷

作者： Fattaheian-Dehkordi, Sajjad Aghaei, Jamshid Amjady, Nima Aalto Univ Dept Elect Engn & Automat Espoo Finland Cent Queensland Univ Sch Engn & Technol Rockhampton Qld Australia Federat Univ Australia Ctr New Energy Transit Res Ballarat Vic Australia

The emergence of multi-agent systems and significant integration of distributed energy sources (DESs) are transforming distribution networks. This necessitates a decentralized management strategy for unbalanced operation in multi-agent distribution systems (MADSs) due to the autonomous nature and potential for unbalanced injection of single-phase DESs. Accordingly, this paper proposes a novel approach for decentralized management of unbalanced operation in MADSs. The approach leverages a customized alternating direction method of multipliers to facilitate decentralized decision-making, while incorporating transactive energy signals aligned with the alternating direction method of multiplier framework to enable independent agent operation. In this scheme, independent agents would optimize their operating costs considering the announced transactive signals, which model the power prices and power loss in the grid. The decentralized structure enables agents to apply stochastic and condition value at risk methods to address the uncertainty and associated risk in scheduling resources. Furthermore, without violating the privacy concerns of agents, the developed transactive-based scheme facilitates minimizing the asymmetrical condition, caused by the unbalanced integration of DESs, in the power request at the connection point of MADSs and transmission networks. Finally, the proposed methodology is simulated on 37-bus and 123-bus test-systems to study its effectiveness in managing the MADSs with unbalanced integration of DESs.

关键词： distribution networks multi-agent systems renewable energy sources decentralised control distributed algorithms distributed control

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Diffusion Generalized Minimum Total Error Entropy Algorithm

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IEEE SIGNAL PROCESSING LETTERS 2025年 32卷 751-755页

作者： Cai, Peng Lin, Dongyuan Qian, Junhui Zheng, Yunfei Wang, Shiyuan Southwest Univ Coll Elect & Informat Engn Chongqing 400715 Peoples R China Chongqing Univ Sch Microelect & Commun Engn Chongqing 400044 Peoples R China Chongqing Univ Chongqing Key Lab Biopercept & Intelligent Informa Chongqing 400044 Peoples R China

Both the minimum error entropy (MEE) and mixture MEE (MMEE) are extensively employed in distributed adaptive filters, exhibiting their robustness against non-Gaussian noise by capturing high-order statistical information from network data. However, the fixed shape of the Gaussian kernel function existing in MEE and MMEE restricts their flexibility, leading to reduced robustness and deteriorated performance. To address this issue, a novel diffusion generalized minimum total error entropy (DGMTE) algorithm is first proposed in this letter, using a generalized MEE criterion to significantly improve the performance of error-in-variables models-based algorithms under non-Gaussian noise. Moreover, as a special case of DGMTE, a generalized minimum total error entropy (GMTE) algorithm is also proposed, and the local convergence analysis of DGMTE is given. Finally, simulations show the superiorities of DGMTE in comparison with other representative algorithms.

关键词： Noise Signal processing algorithms Entropy Shape Adaptation models Kernel Estimation distributed algorithms Cost function Convergence Adaptive filter distributed estimation errors-in-variables model generalized minimum error entropy total least squares

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Decentralized Interference-Aware Codebook Learning in Millimeter Wave MIMO Systems

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IEEE TRANSACTIONS ON COMMUNICATIONS 2025年第5期73卷 3608-3621页

作者： Zhang, Yu Alkhateeb, Ahmed Arizona State Univ Sch Elect Comp & Energy Engn Tempe AZ 85281 USA

Beam codebooks are integral components of future millimeter wave MIMO systems. Therefore, it is critical to optimize these codebooks for efficient and reliable communications. Prior work has focused on single-cell codebook learning problems and under stationary interference. In this work, we generalize the interference-aware codebook learning problem to networks with multiple cells/basestations. One of the key differences is that the underlying environment becomes non-stationary, as the behavior of one basestation may influence the learning of the others. Further, we avoid information exchange between different learning nodes which leads to a fully decentralized system with increased learning difficulties. To tackle the non-stationarity, the averaging of measurements is used to estimate the interference nulling performance of a particular beam, based on which a decision rule is provided. Furthermore, we theoretically justify the adoption of such estimator, and prove that it is a sufficient statistic for the underlying quantity of interest in an asymptotic sense. Finally, a novel reward function is proposed to decouple the learning of the multiple agents running at different nodes. Results show that the developed solution is capable of learning well-shaped codebook patterns for different networks and significantly suppress the interference without requiring any information exchange between basestations.

关键词： Interference Base stations Millimeter wave communication MIMO communication Vectors Information exchange Channel models Symbols Training Synchronization MIMO millimeter wave communication interference cancellation reinforcement learning distributed algorithms

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A Game-Theoretic Approach to Solving the Roman Domination Problem

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IEEE-CAA JOURNAL OF AUTOMATICA SINICA 2025年第5期12卷 1024-1040页

作者： Chen, Xiuyang Tang, Changbing Zhang, Zhao Chen, Guanrong Xinjiang Univ Coll Math & Syst Sci Urumqi 830000 Peoples R China Zhejiang Normal Univ Coll Phys & Elect Informat Engn Jinhua 321004 Peoples R China Zhejiang Normal Univ Coll Math Sci Jinhua 321004 Peoples R China City Univ Hong Kong Dept Elect Engn Hong Kong 999077 Peoples R China

The Roman domination problem is an important combinatorial optimization problem that is derived from an old story of defending the Roman Empire and now regains new significance in cyber space security, considering backups in the face of a dynamic network security requirement. In this paper, firstly, we propose a Roman domination game (RDG) and prove that every Nash equilibrium (NE) of the game corresponds to a strong minimal Roman dominating function (S-RDF), as well as a Pareto-optimal solution. Secondly, we show that RDG is an exact potential game, which guarantees the existence of an NE. Thirdly, we design a game-based synchronous algorithm (GSA), which can be implemented distributively and converge to an NE in O(n) rounds, where $n$ is the number of vertices. In GSA, all players make decisions depending on local information. Furthermore, we enhance GSA to be enhanced GSA (EGSA), which converges to a better NE in O(n(2)) rounds. Finally, we present numerical simulations to demonstrate that EGSA can obtain a better approximate solution in promising computation time compared with state-of-the-art algorithms.

关键词： Games Resource description framework Nash equilibrium Approximation algorithms distributed algorithms Optimization Multi-agent systems Heuristic algorithms Gallium arsenide Faces distributed algorithm game theory multi-agent system potential game Roman dominating function

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Information-Theoretically Private Federated Submodel Learning With Byzantine Local Updates

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ELECTRONICS LETTERS 2025年第1期61卷

作者： Kim, Minchul Hong, Sangwoo Sejong Univ Dept Comp Sci & Engn Seoul South Korea Konkuk Univ Dept Comp Sci & Engn Seoul South Korea

In federated submodel learning (FSL), the aggregator has multiple submodels to train, and each local machine chooses a specific submodel to update with its local training data. Therefore, a local machine may want to conceal which submodel was accessed by the local machine against the aggregator, as well as its local training data. However, Byzantine local updates during the upload phase pose a fundamental threat, as a single adversary can corrupt multiple submodels simultaneously. In this paper, an information-theoretically private FSL scheme for protecting the multiple submodels stored in aggregators against the Byzantine local updates of the upload phase is considered, which has not been considered in previous works.

关键词： distributed algorithms information theory learning (artificial intelligence)

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