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Fast GAMP Algorithm for Nonlinearly Distorted OFDM Signals

IEEE COMMUNICATIONS LETTERS 2021年第5期25卷 1682-1686页

作者： Yang, Chaosan Liu, Xiaobei Guan, Yong Liang Liu, Rongke Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore 639798 Singapore

In this letter, the design of a low-complexity receiver which is robust to orthogonal frequency division multiplex (OFDM) signals with nonlinear clipping distortion is investigated. An approximation of the probability density function (PDF) of the received signals with nonlinear distortion is derived. Based on iteration-dependent approximation of the mean and variance of the clipped signal, a fast generalized approximate message passing (F-GAMP) algorithm is proposed. The proposed F-GAMP achieves bit error rate (BER) performance gain over the conventional OFDM receiver due to the F-GAMP's ability to recover inter-carrier interference (ICI) and exploit frequency diversity induced by the spectral growth of a clipped signal. With almost the same BER performance as the conventional GAMP algorithm, the proposed F-GAMP reduces the computational complexity by a factor of about 3000!

关键词： Nonlinear distortion approximation algorithms Bit error rate Probability density function Peak to average power ratio Receivers Frequency-domain analysis Orthogonal frequency division multiplex (OFDM) peak-to-average power ratio (PAPR) generalized approximate message passing

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Brief Announcement: Minimizing Congestion in Hybrid Demand-Aware Network Topologies 36

Brief Announcement: Minimizing Congestion in Hybrid Demand-A...

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36th International Symposium on Distributed Computing, DISC 2022

作者： Dai, Wenkai Dinitz, Michael Foerster, Klaus-Tycho Schmid, Stefan Faculty of Computer Science Universität Wien Austria Computer Science Department Johns Hopkins University BaltimoreMD United States Computer Science Department Technische Universität Dortmund Germany TU Berlin Germany

ISBN: (纸本)9783959772556

Emerging reconfigurable optical communication technologies enable demand-aware networks: networks whose static topology can be enhanced with demand-aware links optimized towards the traffic pattern the network serves. This paper studies the algorithmic problem of how to jointly optimize the topology and the routing in such demand-aware networks, to minimize congestion. We investigate this problem along two dimensions: (1) whether flows are splittable or unsplittable, and (2) whether routing on the hybrid topology is segregated or not, i.e., whether or not flows either have to use exclusively either the static network or the demand-aware connections. For splittable and segregated routing, we show that the problem is 2-approximable in general, but APX-hard even for uniform demands induced by a bipartite demand graph. For unsplittable and segregated routing, we show an upper bound of O(log m/log log m) and a lower bound of Ω (log m/log log m) for polynomial-time approximation algorithms, where m is the number of static links. Under splittable (resp., unsplittable) and non-segregated routing, even for demands of a single source (resp., destination), the problem cannot be approximated better than Ω (cmax/cmin) unless P=NP, where cmax (resp., cmin) denotes the maximum (resp., minimum) capacity. It is still NP-hard for uniform capacities, but can be solved efficiently for a single commodity and uniform capacities. © Wenkai Dai, Michael Dinitz, Klaus-Tycho Foerster, and Stefan Schmid.

关键词： approximation algorithms

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Incremental SDN Deployment to Achieve Load Balance in ISP Networks 16th

Incremental SDN Deployment to Achieve Load Balance in ISP ...

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16th International Conference on Algorithmic Aspects in Information and Management, AAIM 2022

作者： Cheng, Yunlong Zhou, Hao Gao, Xiaofeng Zheng, Jiaqi Chen, Guihai MoE Key Lab of Artificial Intelligence Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Department of Computer Science and Technology Nanjing University Nanjing China

ISBN: (纸本)9783031160806

Software defined network (SDN) decouples control planes from data planes and integrates them into a logically centralized controller. With capture of the global view, the controller can dynamically and timely reply to the changes of network states. However, replacing the entire traditional networks, e.g., Internet Service Provider (ISP) networks, with SDNs is difficult and computationally expensive. Hence, incremental deployment of partial SDN devices has received much attention. In this paper, we consider the k-LB problem, i.e., upgrading at most k legacy switches to SDN switches to achieve load balance. We claim that k-LB problem is NP-hard and there is no polynomial time (N+ M) 1-ϵ -approximation algorithm for any constant ϵ> 0 unless P= NP, where N (M) is the number of switches (links) in the network. Given these negative results, we propose an effective greedy algorithm and claim that it reaches an approximation guarantee of cavgcminM, where cavg (cmin ) is the average (minimum) link capacity. Large-scale simulations on real ISP networks show that our greedy algorithm achieves near optimal performance and decreases the maximum link utilization by 30% on average compared with the state of the art. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： approximation algorithms

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30th Annual European Symposium on algorithms, ESA 2022

作者： Yang, Sheng Khuller, Samir Choudhary, Sunav Mitra, Subrata Mahadik, Kanak Northwestern University EvanstonIL United States Adobe Research Bangalore India Adobe Research San JoseCA United States

ISBN: (纸本)9783959772471

We study the correlated stochastic knapsack problem of a submodular target function, with optional additional constraints. We utilize the multilinear extension of submodular function, and bundle it with an adaptation of the relaxed linear constraints from Ma [Mathematics of Operations Research, Volume 43(3), 2018] on correlated stochastic knapsack problem. The relaxation is then solved by the stochastic continuous greedy algorithm, and rounded by a novel method to fit the contention resolution scheme (Feldman et al. [FOCS 2011]). We obtain a pseudo-polynomial time (1 - 1/ √ e)/2 0.1967 approximation algorithm with or without those additional constraints, eliminating the need of a key assumption and improving on the (1-1/ 4 √e)/2 0.1106 approximation by Fukunaga et al. [AAAI 2019]. © 2022 Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing. All rights reserved.

关键词： approximation algorithms

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Fair Correlation Clustering in General Graphs 25

Fair Correlation Clustering in General Graphs

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25th International Conference on approximation algorithms for Combinatorial Optimization Problems and the 26th International Conference on Randomization and Computation, APPROX/RANDOM 2022

作者： Schwartz, Roy Zats, Roded The Henry and Marilyn Taub Faculty of Computer Science Technion Haifa Israel

ISBN: (纸本)9783959772495

We consider the family of Correlation Clustering optimization problems under fairness constraints. In Correlation Clustering we are given a graph whose every edge is labeled either with a + or a -, and the goal is to find a clustering that agrees the most with the labels: + edges within clusters and - edges across clusters. The notion of fairness implies that there is no over, or under, representation of vertices in the clustering: every vertex has a color and the distribution of colors within each cluster is required to be the same as the distribution of colors in the input graph. Previously, approximation algorithms were known only for fair disagreement minimization in complete unweighted graphs. We prove the following: (1) there is no finite approximation for fair disagreement minimization in general graphs unless P = NP (this hardness holds also for bicriteria algorithms);and (2) fair agreement maximization in general graphs admits a bicriteria approximation of ≈ 0.591 (an improved ≈ 0.609 true approximation is given for the special case of two uniformly distributed colors). Our algorithm is based on proving that the sticky Brownian motion rounding of [Abbasi Zadeh-Bansal-Guruganesh-Nikolov-Schwartz-Singh SODA'20] copes well with uncut edges. © Roy Schwartz and Roded Zats.

关键词： approximation algorithms

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Half-Guarding Weakly-Visible Polygons and Terrains 42

Half-Guarding Weakly-Visible Polygons and Terrains

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42nd IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science, FSTTCS 2022

作者： Duraisamy, Nandhana Hillberg, Hannah Miller Jallu, Ramesh K. Krohn, Erik Maheshwari, Anil Nandy, Subhas C. Pahlow, Alex PSG College of Technology Coimbatore India University of Wisconsin OshkoshWI United States Indian Institute of Information Technology Raichur India Carleton University Ottawa Canada Indian Statistical Institute Kolkata India

ISBN: (纸本)9783959772617

We consider a variant of the art gallery problem where all guards are limited to seeing 180°. Guards that can only see in one direction are called half-guards. We give a polynomial time approximation scheme for vertex guarding the vertices of a weakly-visible polygon with half-guards. We extend this to vertex guarding the boundary of a weakly-visible polygon with half-guards. We also show NP-hardness for vertex guarding a weakly-visible polygon with half-guards. Lastly, we show that the orientation of half-guards is critical in terrain guarding. Depending on the orientation of the half-guards, the problem is either very easy (polynomial time solvable) or very hard (NP-hard). © Nandhana Duraisamy, Hannah Miller Hillberg, Ramesh K. Jallu, Erik Krohn, Anil Maheshwari, Subhas C. Nandy, and Alex Pahlow

关键词： approximation algorithms

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Fairness-Aware algorithms for Seed Allocation in Social Advertising 24

Fairness-Aware Algorithms for Seed Allocation in Social Adve...

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Wang, Pengzi Zhu, Yiming Han, Kai Yin, Zhizhuo Xiu, Qing Hui, Pan University of Science and Technology of China China Hong Kong University of Science and Technology Hong Kong Hong Kong School of Computer Science and Technology Soochow University China

ISBN: (纸本)9798350319934

As a crucial and widely researched application in social networks, social advertising refers to selecting seed users for several advertisers to propagate their advertisements in the network via a information cascade effect. Prior studies on this topic have presented approximation algorithms merely for maximizing the expected revenue. However, regarding to the fairness issue, no existing works have provided effective solution. Such a issue would cause polarized revenues among different advertisers and the draining of them. In this paper, we investigate the fairness issue in social advertising. That is, how the social network platform owner distributes seeds users fairly to different advertisers. We define the fairness metric according to the maximin share, a concept about fair distribution in computation economics, and develop a novel approximation algorithm. Our approach achieves an approximation factor of (1-1/e)2/3 1/h2, where h is the number of different advertisers. Our result recovers the best-known proposed (1-1/e)2/2. -approximation ratio when h=1 and extends settings to a more general case. The evaluation on real datasets indicates that our solution is effective and outperforms the existing algorithms in terms of fairness. © 2022 IEEE.

关键词： approximation algorithms

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Multiscale Quantum Gradual approximation Algorithm: An Optimization Algorithm With a Step-by-Step approximation Strategy

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IEEE ACCESS 2020年 8卷 71548-71557页

作者： Cheng, Kun Wang, Peng Li, Zhendong Univ Chinese Acad Sci Beijing 1UUU49 Peoples R China Chinese Acad Sci Chengdu Inst Comp Applicat Beijing 100864 Peoples R China Southwest Minzu Univ Sch Comp Sci & Technol Chengdu 610041 Peoples R China Ningxia Univ Sch Informat Engn Yinchuan 750021 Ningxia Peoples R China Ningxia Municipal & Minist Educ Collaborat Innovat Ctr Ningxia Big Data & Artific Yinchuan 750021 Ningxia Peoples R China

In quantum swarm intelligence algorithms, the tunneling effect of the particles is determined by the potential energy acting on the particles. The tunneling effect of the particles affects the global search ability and convergence speed of the algorithm. Quantum algorithms with a single potential energy are prone to premature convergence under certain complex test functions. In this paper, we propose a multiscale quantum gradual approximation algorithm (MQGAA), which simply uses different approximation strategies to obtain different potential energy functions, to solve the premature problem of the optimization algorithm. In the MQGAA, particles undergo a transition from an unconstrained state to a constrained state at each scale. To demonstrate the effectiveness of the proposed algorithm, experiments are carried out with several common and effective stochastic algorithms on N-dimensional double-well potential functions and classical benchmark functions. We also use the Wilcoxon rank test to detect the performance of MQGAA. The experimental results show that the algorithm using a step-by-step approximation strategy achieves a better optimization performance on some complex test functions.

关键词： approximation algorithms Optimization Potential energy Linear programming Particle swarm optimization Tunneling Taylor approximation unconstrained state constrained state multiscale multiscale quantum harmonic oscillator algorithm

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Near-Field Localization for Non-circular Sources in the Presence of Sensor Phase Uncertainties

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IEEE WIRELESS COMMUNICATIONS LETTERS 2021年第3期10卷 562-566页

作者： Shu, Ting Li, Linna He, Jin Shanghai Jiao Tong Univ Shanghai Key Lab Intelligent Sensing & Recognit Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China Shanghai Aerosp Elect Commun Equipment Res Inst Dept Radar Res Ctr Shanghai 201109 Peoples R China

We address the problem of locating multiple near-field non-circular sources in the presence of sensor phase errors. Such phase error may arise in MIMO applications where perfect phase synchronization is not achieved. Using source non-circularity and based on the virtual ESPRIT idea, we define three cumulant matrices to form two matrix pencils, whose generalized eigenvalues are dependent only on the amplitude but not on the phase of the near-field propagation geometry. We introduce a linear operator to estimate the generalized eigenvalues of the defined matrix pencils, and then apply our recently proposed SPatial Amplitude Ratio based Estimator (SPARE) approach to estimate angle and range parameters of the sources. We define the presented algorithm as Rotational SPARE (R-SPARE) since it obtains the spatial amplitudes from the rotational structures embedded in the defined matrices. R-SPARE shares all advantages offered by the SPARE. We finally examine the performance of R-SPARE via numerical simulations.

关键词： Signal processing algorithms Eigenvalues and eigenfunctions Sensor arrays Geometry approximation algorithms Phased arrays Manifolds Array signal processing near-field source localization phase error spatial amplitude ratio

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Improved approximation for Fractionally Subadditive Network Design

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INFORMATION PROCESSING LETTERS 2020年 154卷

作者： Mucha, Marcin Smulewicz, Marcin Univ Warsaw Inst Informat Warsaw Poland

Recently, Guruganesh et al. [1] proposed the Fractionally Subadditive Network Design (f-SAND) problem. In this problem, we are given a weighted graph G with a special root vertex r together with k subsets of its vertices called scenarios. We need to assign capacities to the edges of G so that for every scenario, the assigned capacities support a unit flow from the root to all vertices in the scenario, simultaneously. The goal is to minimize the total cost of the solution. f-SAND is a fairly natural problem and it generalizes several well -studied problems, such as the Steiner Tree problem. The added interest to this problem stems from the fact that standard tools used for approximating network design problems do not seem to apply to f-SAND. Despite this, Guruganesh et al. conjecture the existence of an algorithm with an approximation ratio bounded by a constant, i.e. independent of k. They make a step towards this goal by giving a 3 -approximation algorithm for k = 2. In this paper, we present an algorithm similar to that of [1], together with an analysis that not only yields an improved approximation ratio of 4, but is also significantly simpler. We also generalize it to arbitrary k, giving a 4k-approximation. On the negative side, we argue that without additional insights, the techniques used are not likely to produce a constant factor approximation algorithm. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Flow approximation algorithms Network design

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