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51st International Colloquium on Automata, Languages, and Programming, ICALP 2024

作者： Abbasi, Fateme Bosch-Calvo, Miguel Grandoni, Fabrizio Tinguely, Antoine Adamczyk, Marek Byrka, Jaroslaw Sornat, Krzysztof University of Wroclaw Poland IDSIA USI-SUPSI Lugano Switzerland AGH University Kraków Poland

ISBN: (纸本)9783959773225

In the Submodular Facility Location problem (SFL) we are given a collection of n clients and m facilities in a metric space. A feasible solution consists of an assignment of each client to some facility. For each client, one has to pay the distance to the associated facility. Furthermore, for each facility f to which we assign the subset of clients Sf, one has to pay the opening cost g(Sf), where g(·) is a monotone submodular function with g(∅) = 0. SFL is APX-hard since it includes the classical (metric uncapacitated) Facility Location problem (with uniform facility costs) as a special case. Svitkina and Tardos [35, SODA’06] gave the current-best O(log n) approximation algorithm for SFL. The same authors pose the open problem whether SFL admits a constant approximation and provide such an approximation for a very restricted special case of the problem. We make some progress towards the solution of the above open problem by presenting an O(log log n) approximation. Our approach is rather flexible and can be easily extended to generalizations and variants of SFL. In more detail, we achieve the same approximation factor for the natural generalizations of SFL where the opening cost of each facility f is of the form pf + g(Sf) or wf · g(Sf), where pf, wf ≥ 0 are input values. We also obtain an improved approximation algorithm for the related Universal Stochastic Facility Location problem. In this problem one is given a classical (metric) facility location instance and has to a priori assign each client to some facility. Then a subset of active clients is sampled from some given distribution, and one has to pay (a posteriori) only the connection and opening costs induced by the active clients. The expected opening cost of each facility f can be modelled with a submodular function of the set of clients assigned to f. © Fateme Abbasi, Marek Adamczyk, Miguel Bosch-Calvo, Jaroslaw Byrka, Fabrizio Grandoni, Krzysztof Sornat, and Antoine Tinguely.

关键词： approximation algorithms

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RSMA-Enhanced Physical Layer Security for ISAC Systems

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IEEE WIRELESS COMMUNICATIONS LETTERS 2025年第4期14卷 1064-1068页

作者： Zhao, Bo Qiu, Tian Ren, Guangliang Jin, Zengwang Liu, Zhiqiang Northwestern Polytech Univ Sch Cybersecur Xian 710072 Peoples R China Northwestern Polytech Univ Collaborat Innovat Ctr Shanghai 201100 Peoples R China Northwestern Polytech Univ Taicang Yangtze River Delta Res Inst Suzhou 215400 Peoples R China Xidian Univ Sch Telecommun Engn Xian 710071 Peoples R China

In this letter, we enhance the physical layer security (PLS) of integrated sensing and communication (ISAC) systems using rate-splitting multiple access (RSMA). To ensure specific security requirements, we formulate a joint optimization problem that involves the precoding matrix and the common rate. The goal is to maximize the communication-sensing weighted secure sum rate (SSR), thereby enhancing the overall system performance while maintaining security. To solve the non-convex optimization problem, we adopt the staged algorithm named successive convex approximation with penalty (SCA-P) algorithm. Simulation results show that the proposed scheme has better security performance than the state-of-the-art schemes.

关键词： Integrated sensing and communication Security Vectors Precoding Optimization Eavesdropping approximation algorithms Physical layer security Noise NOMA PLS RSMA ISAC weighted SSR maximization

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Boosting On-Policy Actor-Critic With Shallow Updates in Critic

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第4期PP卷 6981-6994页

作者： Li, Luntong Zhu, Yuanheng Chinese Acad Sci Inst Automat State Key Lab Multimodal Artificial Intelligence S Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

Deep reinforcement learning (DRL) benefits from the representation power of deep neural networks (NNs), to approximate the value function and policy in the learning process. Batch reinforcement learning (BRL) benefits from stable training and data efficiency with fixed representation and enjoys solid theoretical analysis. This work proposes least-squares deep policy gradient (LSDPG), a hybrid approach that combines least-squares reinforcement learning (RL) with online DRL to achieve the best of both worlds. LSDPG leverages a shared network to share useful features between policy (actor) and value function (critic). LSDPG learns policy, value function, and representation separately. First, LSDPG views deep NNs of the critic as a linear combination of representation weighted by the weights of the last layer and performs policy evaluation with regularized least-squares temporal difference (LSTD) methods. Second, arbitrary policy gradient algorithms can be applied to improve the policy. Third, an auxiliary task is used to periodically distill the features from the critic into the representation. Unlike most DRL methods, where the critic algorithms are often used in a nonstationary situation, i.e., the policy to be evaluated is changing, the critic in LSDPG is working on a stationary case in each iteration of the critic update. We prove that, under some conditions, the critic converges to the regularized TD fixpoint of current policy, and the actor converges to the local optimal policy. The experimental results on challenging Procgen benchmark illustrate the improvement of sample efficiency of LSDPG over proximal policy optimization and phasic policy gradient (PPG).

关键词： Artificial neural networks Vectors Task analysis Training Representation learning approximation algorithms Optimization Actor-critic deep reinforcement learning (DRL) proximal policy optimization (PPO) shallow reinforcement learning (SRL)

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Automated Category Tree Construction: Hardness Bounds and algorithms

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ACM TRANSACTIONS ON DATABASE SYSTEMS 2024年第3期49卷 1-32页

作者： Gershtein, Shay Avron, Uri Guy, Ido Milo, Tova Novgorodov, Slava Tel Aviv Univ Tel Aviv Israel Ben Gurion Univ Negev Beer Sheva Israel

Category trees, or taxonomies, are rooted trees where each node, called a category, corresponds to a set of related items. The construction of taxonomies has been studied in various domains, including e-commerce, document management, and question answering. Multiple algorithms for automating construction have been proposed, employing a variety of clustering approaches and crowdsourcing. However, no formal model to capture such categorization problems has been devised, and their complexity has not been studied. To address this, we propose in this work a combinatorial model that captures many practical settings and show that the aforementioned empirical approach has been warranted, as we prove strong inapproximability bounds for various problem variants and special cases when the goal is to produce a categorization of the maximum utility. In our model, the input is a set of n weighted item sets that the tree would ideally contain as categories. Each category, rather than perfectly match the corresponding input set, is allowed to exceed a given threshold for a given similarity function. The goal is to produce a tree that maximizes the total weight of the sets for which it contains a matching category. A key parameter is an upper bound on the number of categories an item may belong to, which produces the hardness of the problem, as initially each item may be contained in an arbitrary number of input sets. For this model, we prove inapproximability bounds, of order and special cases, loosely justifying the aforementioned heuristic approach. Our work includes reductions based on parameterized randomized constructions that highlight how various problem parameters and properties of the input may affect the hardness. Moreover, for the special case where the category must be identical to the corresponding input set, we devise an algorithm whose approximation guarantee depends solely on a more granular parameter, allowing improved worst-case guarantees, as well as the applicat

关键词： Maximum independent set approximation algorithms approximation hardness bounds taxonomy construction category tree construction

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Grundy Number-Based Optimal Sensor Placement in 3D Wireless Sensor Network

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IEEE ACCESS 2024年 12卷 148502-148515页

作者： Afizudeen, S. Pavithra, R. Vellore Inst Technol Chennai 600127 Tamil Nadu India

Wireless Sensor Networks (WSNs) are widely used in urban and commercial sectors to collect information over a substantial region using intelligent inter-linked sensors. The deployment of excessive number of sensors leads to data redundancy and consumes more energy. An Efficient number of required sensors with optimal position for a certain region contains predefined set of targets, maximizes the WSN lifetime and minimizes redundancy of sensors. Hence in this study, a Grundy Number-based Deterministic Sensor Deployment (GN-DSD) algorithm is proposed to identify the sensor requirement based on target's spatial coordinates and the optimal sensors coordinates to resolve Q-coverage problem in 3D-terrain. To enhance the connectivity of WSN a relay node deployment method is proposed with Breadth First Search approach. The performance of proposed algorithm is tested by a series of simulation with varying the parameters, and a statistical analysis is performed using Analysis of Variance (ANOVA) and Fisher's Least Significant Difference (LSD) Post-Hoc test.

关键词： Robot sensing systems Wireless sensor networks Monitoring Three-dimensional displays Sensor placement Relays approximation algorithms Color Simulation Quality of service Grundy number network connectivity sensor requirement sequential vertex coloring target Q-coverage

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ULBRF: A Framework for Maximizing Influence in Dynamic Networks Based on Upper and Lower Bounds of Propagation

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年第6期11卷 6704-6717页

作者： Liu, Zekun Yu, Jianyong Liang, Wei Han, Xue Hunan Univ Sci & Technol Sanya Inst Sanya 572024 Peoples R China

The influence maximization problem that selects a set of seed nodes to maximize the influence spread has been becoming a hot research topic. The classical algorithms select the seed nodes at the initial moment based on the topological properties in static networks, which are not suitable for solving the problem in dynamic networks. In this paper, we propose the Upper and Lower Bound Radix Forward framework to solve this problem in the dynamic network. First, we propose the upper bound of the influence spread and derive the corresponding lower bound to find the seed nodes. When extending the classical IM to the dynamic network, we find the problem that the final influence spreads increase logarithmically with the linear expansion of the initial seed sets. Therefore, the Dynamic Network Path Dispersion is proposed to solve the problem by measuring the heterogeneity of the snaphots on the dynamic network. The Upper and Lower Bound Radix Forward (ULBRF) is tested and applied in two real dynamic networks and two synthetic networks. Experimental results show that the ULBRF is better than the widely recognized improved greedy algorithms. The Dynamic Network Path Dispersion (DNPD) and the strategy based on the heterogeneities also expand the final influence spread by 35% similar to 45%.

关键词： Heuristic algorithms Integrated circuit modeling Upper bound Greedy algorithms Dispersion Adaptation models Optimization Diffusion models approximation algorithms Vectors Influence maximization dynamic network social network greedy algorithm optimization

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WMMSE-Based Processing in Cell-Free Massive MIMO Systems

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IEEE WIRELESS COMMUNICATIONS LETTERS 2025年第2期14卷 330-334页

作者： Fu, Jiafei Mobini, Zahra Ngo, Hien Quoc Zhu, Pengcheng Matthaiou, Michail Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Queens Univ Belfast Ctr Wireless Innovat Belfast BT3 9DT North Ireland

In this letter, we address the weighted sum-rate maximization problem in a cell-free massive multi-input multi-output (CF-mMIMO) system, subject to constraints on the minimum individual quality of service (QoS), maximum power consumption at each access point (AP), and maximum fronthaul capacity. By harnessing the low computational complexity weighted minimum mean square error (WMMSE) framework, two algorithms are proposed to solve the formulated mixed integer nonlinear programming (MINLP) problems with instantaneous/statistical channel state information (CSI). Our instantaneous CSI-based approach can be applied to jointly optimize the power control, precoding, and user association, while the statistical CSI-based approach can be utilized to jointly optimize the power control and user association. Simulation results demonstrate that the proposed instantaneous CSI-based algorithm can provide approximately 66.72% sum-rate gain compared to the scheme with random user association, equal power allocation, and fixed local MMSE-based precoding design. Additionally, the statistical CSI-based algorithm offers competitive performance compared with the instantaneous CSI-based algorithm.

关键词： Precoding Power control Quality of service Uplink Channel estimation Optimization Downlink Data communication approximation algorithms Vectors Cell-free massive MIMO weighted minimum mean square error weighted sum-rate maximization

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Compression of Multichannel Signals With Irregular Sampling Rates and Data Gaps

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IEEE ACCESS 2024年 12卷 193195-193211页

作者： Cervenansky, Pablo Martin, Alvaro Seroussi, Gadiel PEDECIBA Informat Montevideo 11800 Uruguay Univ Republica Fac Ingn Inst Comp Montevideo 11300 Uruguay Univ Republica Fac Ingn Inst Ingn Elect Montevideo 11300 Uruguay

In this work, we study the compression of multichannel signals with irregular sampling rates and data gaps. We consider state-of-the-art algorithms, which were originally designed to compress gapless signals with regular sampling, adapt them to operate with signals with irregular sampling rates and data gaps, and then evaluate their performance experimentally, through the compression of signals obtained from real-world datasets. Both the original algorithms and our schemes compress signals by exploiting their temporal, and, in some cases, spatial correlation. They work in a near-lossless fashion, guaranteeing a bounded absolute error between each decompressed sample and its original value. This includes the important lossless compression case, which corresponds to an error bound of zero. Our schemes first encode the position of the gaps, using arithmetic coding combined with a Krichevsky-Trofimov probability assignment on a Markov model, and then encode the data values separately. Our experimental analysis consists of comparing the compression performance of our schemes with each other, and with representative special-purpose and general-purpose lossless compression algorithms. We also measure and compare the schemes' running times, to assess their practicality. From the results we extract some general conclusions: in the lossless case, TS2Diff and LZMA attain the best compression performance, whereas our adaptation of algorithm APCA is preferred for positive error bounds. At the same time, our adaptation of APCA, and TS2Diff, attain some of the best running times.

关键词： Encoding Correlation Image coding approximation algorithms Data models Compression algorithms Principal component analysis Time series analysis Classification algorithms Arrays Multichannel signal compression time series compression near-lossless compression irregular sampling rate data gaps

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User-IRS Association for Sum-Rate Maximization in Multi-IRS Aided Wireless Communication Networks

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IEEE ACCESS 2024年 12卷 167224-167235页

作者： Suhail Khan, Zarghuna Mirza, Jawad Obidallah, Waeal J. Alkhathami, Mohammed Alsadie, Deafallah Alsuwailem, Rawan COMSATS Univ Islamabad Dept Elect & Comp Engn Islamabad 45550 Pakistan Imam Mohammad Ibn Saud Islamic Univ IMSIU Coll Comp & Informat Sci Informat Syst Dept Riyadh 11673 Saudi Arabia Umm Al Qura Univ Dept Comp Sci & Artificial Intelligence Mecca 21961 Saudi Arabia

In this paper, we investigate the assignment of intelligent reflecting surfaces (IRSs) to user-base station (BS) pairs in a multi-IRS-assisted wireless communication network. Our objective is to optimize the allocation of one or more IRSs to each user-BS pair to maximize the overall sum-rate of the network. Using passive beamforming, the transmitted signal is directed towards each user through single or multiple IRSs. To achieve optimal user-IRS allocation, we employ the modified Hungarian algorithm. Additionally, we propose a greedy algorithm for user-IRS association, which offers lower complexity than the modified Hungarian algorithm while still aiming to maximize the sum-rate of the network. Simulation results demonstrate the superiority of modified Hungarian algorithm over other techniques. It has been observed that assigning two IRSs per user-BS pair significantly improves the sum-rate compared to assigning a single IRS per user-BS pair. In particular, with the transmission power of 10 dB and 128 reflection coefficients, the modified Hungarian algorithm achieves a sum-rate improvement of approximately 11.6% compared to the greedy algorithm and 20.6% compared to the random IRS assignment.

关键词： Array signal processing Wireless communication 6G mobile communication Optimization MISO communication approximation algorithms Signal to noise ratio Relays Phased arrays Millimeter wave communication Intelligent reflecting surface Hungarian algorithm sum-rate user association

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Joint Task Offloading and Resource Allocation in Heterogeneous Edge Environments

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第6期23卷 7318-7334页

作者： Liu, Yu Mao, Yingling Liu, Zhenhua Ye, Fan Yang, Yuanyuan SUNY Stony Brook Dept Elect & Comp Engn Stony Brook NY 11794 USA SUNY Stony Brook Dept Appl Math & Stat Stony Brook NY 11794 USA

Mobile edge computing has emerged as a prevalent computing paradigm to support applications that demand low latency and high computational capacity. Hardware reconfigurable accelerators exhibit high energy efficiency and low latency compared to general-purpose servers, making them ideal for integration into mobile edge computing systems. This article investigates the problem of joint task offloading, access point selection, and resource allocation in heterogeneous edge environments for latency minimization. Given the heterogeneity of edge computing devices and the interdependence of the decisions required for offloading, access point selection, and resource allocation, it is challenging to optimize over them simultaneously. We decomposed the proposed problem into two disjoint subproblems and developed algorithms for each of them. The first subproblem is to jointly determine access point selection and communication resource allocation decisions, for which we have proposed an algorithm with a provable approximation ratio of 2.62/(1-8 lambda), where lambda is a tunable parameter balancing the approximation ratio and time complexity. Additionally, we offer a faster variant of the algorithm with an approximation ratio of (root 3+1)(2). The second subproblem is to determine offloading and computing resource allocation decisions jointly and is NP-hard, where we developed algorithms based on relaxation and rounding. We conducted comprehensive numerical simulations to evaluate the proposed algorithms, and the results demonstrated that our algorithms outperformed existing baselines and achieved near-optimal performance across various settings.

关键词： Task analysis Resource management Field programmable gate arrays approximation algorithms Wireless communication Servers Reinforcement learning Heterogeneous edge environments latency minimization mobile edge computing

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