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An Improved Kramers-Kronig Receiver With Upsampling-Free and Low Carrier-to-Signal Power Ratio

JOURNAL OF LIGHTWAVE TECHNOLOGY 2024年第19期42卷 6731-6738页

作者： Luo, Shiwei Li, Jianping Yu, Xinkuo Qin, Yuwen Lin, Ning Xu, Ou Xiang, Meng Fu, Songnian Guangdong Univ Technol Inst Adv Photon Technol Sch Informat Engn Guangzhou 510006 Peoples R China Guangdong Prov Key Lab Photon Informat Technol Guangzhou 510006 Peoples R China Guangdong Commun Serv Co Ltd Guangzhou 510630 Peoples R China

The Kramers-Kronig (KK) receiver breaks through the limitation that a single photodetector (PD) can only process the intensity information, and has become a research hotspot in optical communication system. However, in the conventional KK algorithm, the required nonlinear operations (such as logarithm (log) and exponential and square-root (sqrt) functions) significantly broadens the signal spectrum, it is necessary to employ the digital upsampling at the beginning of the digital signal processing (DSP). The high sampling rate will bring multiple hardware resources and power consumption, which becomes a major obstacle to implement KK receiver. In this paper, we propose a novel improved algorithm for the KK receiver, which does not need the digital upsampling and lowers the required carrier-to-signal power ratio (CSPR) with a comparison of the typical upsampling-free KK receiver. In the proposed improved algorithm, we adopt the mathematical approximations of Taylor's expansion and Newton's tangent method to avoid the use of log and sqrt functions. Then we removed the exponential function by expressing complex signal in Cartesian form (i.e., real plus imaginary). Whereas the real part of it is induced by employing imaginary part-induced signal-signal beat interference (SSBI) removal and sqrt operation to recover. Meanwhile, we use the simplified hybrid KK-SSBI cancellation (SHKK-SSBIC) technology to reduce the required CSPR by the system. We validate our proposed algorithm by transmitting 160 Gb/s single-sideband (SSB) signal. The experimental results show that compared with the upsampling-free KK receiver, the proposed scheme can realize the reduction of the required CSPR by 1 dB and 0.8 dB respectively under the Nyquist sampling rate at (back-to-back) BTB and 80 km transmission. Moreover, our proposed method achieves a 2 dB system sensitivity improvement in the BTB scenario. We also discuss the hardware implementation of the improved KK algorithm and its computationa

关键词： Amplitude modulation Sensitivity Optical receivers Optical imaging Signal processing algorithms Costs approximation algorithms Kramers-Kronig receiver Newton's tangent method upsampling-free

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Computing nine new best-so-far solutions for capacitated VRP with a cellular genetic algorithm

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INFORMATION PROCESSING LETTERS 2006年第6期98卷 225-230页

作者： Alba, Enrique Dorronsoro, Bernabe Univ Malaga Dept Comp Sci E-29071 Malaga Spain

The Vehicle Routing Problem (VRP) is a hard combinatorial problem with numerous industrial applications. Among the large number of extensions to the canonical VRP we Study the Capacitated VRP (CVRP), which is mainly characterized by Using vehicles of the same capacity. A Cellular Genetic Algorithm (cGA)-a kind of decentralized Population based heuristic-is used for solving, CVRP. improving several of the best existing results SO far in the literature. Our study shows a high performance in terms of the quality of the solutions found and the number of function evaluations (effort). (c) 2006 Elsevier B.V. All rights reserved.

关键词： cellular genetic algorithm hybrid genetic algorithm vehicle routing problem algorithms approximation algorithms design of algorithms

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An Ant Colony Inspired Multi-Bernoulli Filter for Cell Tracking in Time-Lapse Microscopy Sequences

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2020年第6期24卷 1703-1716页

作者： Xu, Benlian Lu, Mingli Cong, Jinliang Nener, Brett D. Changshu Inst Technol Sch Mech Engn Suzhou 215500 Peoples R China Changshu Inst Technol Sch Elect & Automat Engn Suzhou 215500 Peoples R China Univ Western Australia Dept Elect Elect & Comp Engn Perth WA 6009 Australia

The analysis of the dynamic behavior of cells in time-lapse microscopy sequences requires the development of reliable and automatic tracking methods capable of estimating individual cell states and delineating the lineage trees corresponding to the tracks. In this paper, we propose a novel approach, i.e., an ant colony inspired multi-Bernoulli filter, to handle the tracking of a collection of cells within which mitosis, morphological change and erratic dynamics occur. The proposed technique treats each ant colony as an independent one in an ant society, and the existence probability of an ant colony and its density distribution approximation are derived from the individual pheromone field and the corresponding heuristic information for the approximation to the multi-Bernoulli parameters. To effectively guide ant foraging between consecutive frames, a dual prediction mechanism is proposed for the ant colony and its pheromone field. The algorithm performance is tested on challenging datasets with varying population density, frequent cell mitosis and uneven motion over time, demonstrating that the algorithm outperforms recently reported approaches.

关键词： Tracking Informatics approximation algorithms Radio frequency Indexes Heuristic algorithms Prediction algorithms Ant colony multi-Bernoulli cell tracking cell lineage tree pheromone field

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Solving NP-hard semirandom graph problems in polynomial expected time

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JOURNAL OF algorithms-COGNITION INFORMATICS AND LOGIC 2007年第1期62卷 19-46页

作者： Coja-Oghlan, Amin Humboldt Univ Inst Informat D-10099 Berlin Germany

The aim of this paper is to present an SDP-based algorithm for finding a sparse induced subgraph of order Theta (n) hidden in a semirandom graph of order n. As an application we obtain an algorithm that requires not more than O(n) random edges in order to k-color a semirandom k-colorable graph within polynomial expected time, thereby extending results of Feige and Kilian [J. Comput. System Sci. 63 (2001) 639-671] and of Stibramanian [J. algorithms 33 (1999) 112-123]. (c) 2004 Elsevier Inc. All rights reserved.

关键词： approximation algorithms CHROMATIC NUMBER CLIQUE approximation algorithms iodine algorithms Polynomial Line graph Nondeterministic polynomial-time hard

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On the Longest Common Rigid Subsequence Problem

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ALGORITHMICA 2010年第2期56卷 270-280页

作者： Bansal, Nikhil Lewenstein, Moshe Ma, Bin Zhang, Kaizhong IBM Corp TJ Watson Res Ctr Yorktown Hts NY 10598 USA Bar Ilan Univ Dept Comp Sci IL-52900 Ramat Gan Israel Univ Western Ontario Dept Comp Sci London ON N6A 5B7 Canada

The longest common subsequence problem (LCS) and the closest substring problem (CSP) are two models for finding common patterns in strings, and have been studied extensively. Though both LCS and CSP are NP-Hard, they exhibit very different behavior with respect to polynomial time approximation algorithms. While LCS is hard to approximate within n (delta) for some delta > 0, CSP admits a polynomial time approximation scheme. In this paper, we study the longest common rigid subsequence problem (LCRS). This problem shares similarity with both LCS and CSP and has an important application in motif finding in biological sequences. We show that it is NP-hard to approximate LCRS within ratio n (delta) , for some constant delta > 0, where n is the maximum string length. We also show that it is NP-Hard to approximate LCRS within ratio Omega(m), where m is the number of strings.

关键词： Longest common subsequence Longest common rigid subsequence approximation algorithms Motif finding Pattern matching and computational biology

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PAC Reinforcement Learning Algorithm for General-Sum Markov Games

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2023年第5期68卷 2821-2831页

作者： Zehfroosh, Ashkan Tanner, Herbert G. Univ Delaware Dept Mech Engn Newark DE 19716 USA

This article presents a theoretical framework for probably approximately correct (PAC) multi-agent reinforcement learning (MARL) algorithms for Markov games. Using the idea of delayed Q-learning, this article extends the well-known Nash Q-learning algorithm to build a new PAC MARL algorithm for general-sum Markov games. In addition to guiding the design of a provably PAC MARL algorithm, the framework enables checking whether an arbitrary MARL algorithm is PAC. Comparative numerical results demonstrate the algorithm's performance and robustness.

关键词： Games Markov processes Picture archiving and communication systems Nash equilibrium Q-learning approximation algorithms Convergence Markov game multiagent system probably approximately correct (PAC) reinforcement learning

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Pure Nash Equilibrium in a Capacitated Selfish Resource Allocation Game

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IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2018年第1期5卷 536-547页

作者： Etesami, Seyed Rasoul Basar, Tamer Univ Illinois Coordinated Sci Lab Urbana IL 61801 USA

A resource allocation game with identical preferences is considered where each player, as a node of an undirected un-weighted network, tries to minimize his or her cost by caching an appropriate resource. Using a generalized ordinal potential function, a polynomial time algorithm is devised in order to obtain a pure-strategy Nash equilibrium (NE) when the number of resources is limited or the network has a high edge density with respect to the number of resources. Moreover, an algorithm to approximate any NE of the game over general networks is provided, and the results are extended to games with arbitrary cache sizes. Finally, a connection between graph coloring and the NE points has been established.

关键词： Games Resource management Nash equilibrium approximation algorithms Control systems Social network services Context

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A Complexity Theoretical Study of Fuzzy K-Means

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ACM TRANSACTIONS ON algorithms 2020年第4期16卷 1–25页

作者： Bloemer, Johannes Brauer, Sascha Bujna, Kathrin Paderborn Univ Dept Comp Sci Warburger Str 100 D-33098 Paderborn Germany

The fuzzy K-means problem is a popular generalization of the well-known K-means problem to soft clusterings. In this article, we present the first algorithmic study of the problem going beyond heuristics. Our main result is that, assuming a constant number of clusters, there is a polynomial time approximation scheme for the fuzzy K-means problem. As a part of our analysis, we also prove the existence of small coresets for fuzzy K-means. At the heart of our proofs are two novel techniques developed to analyze the otherwise notoriously difficult fuzzy K-means objective function.

关键词： Clustering fuzzy K-means approximation algorithms coresets

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Influence Maximization in Complex Networks by Using Evolutionary Deep Reinforcement Learning

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2023年第4期7卷 995-1009页

作者： Ma, Lijia Shao, Zengyang Li, Xiaocong Lin, Qiuzhen Li, Jianqiang Leung, Victor C. M. Nandi, Asoke K. Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China Brunel Univ London Dept Elect & Elect Engn Uxbridge UB8 3PH Middx England Shenzhen Univ Shenzhen 518060 Peoples R China

Influence maximization (IM) in complex networks tries to activate a small subset of seed nodes that could maximize the propagation of influence. The studies on IM have attracted much attention due to their wide applications such as item recommendation, viral marketing, information propagation and disease immunization. Existing works mainly model the IM problem as a discrete optimization problem, and use either approximate or meta-heuristic algorithms to address this problem. However, these works are hard to find a good tradeoff between effectiveness and efficiency due to the NP-hard and large-scale network properties of the IM problem. In this article, we propose an evolutionary deep reinforcement learning algorithm (called EDRL-IM) for IM in complex networks. First, EDRL-IM models the IM problem as a continuous weight parameter optimization of deep Q network (DQN). Then, it combines an evolutionary algorithm (EA) and a deep reinforcement learning algorithm (DRL) to evolve the DQN. The EA simultaneously evolves a population of individuals, and each of which represents a possible DQN and returns a solution to the IM problem through a dynamic markov node selection strategy, while the DRL integrates all information and network-specific knowledge of DQNs to accelerate their evolution. Systematic experiments on both benchmark and real-world networks show the superiority of EDRL-IM over the state-of-the-art IM methods in finding seed nodes.

关键词： Optimization Integrated circuit modeling Complex networks Computational modeling approximation algorithms Reinforcement learning Metaheuristics Complex networks influence maximization deep reinforcement learning evolutionary algorithm optimization

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Reliability-Aware Virtualized Network Function Services Provisioning in Mobile Edge Computing

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2020年第11期19卷 2699-2713页

作者： Huang, Meitian Liang, Weifa Shen, Xiaojun Ma, Yu Kan, Haibin Australian Natl Univ Res Sch Comp Sci Canberra ACT 2601 Australia Univ Missouri Sch Comp & Engn Kansas City MO 64110 USA Fudan Univ Sch Comp Sci Shanghai Key Lab Intelligent Informat Proc Shanghai 200433 Peoples R China Inst Adv Commun & Data Sci Fudan Zhongan Joint Lab Blockchain & Informat Sec Shanghai Peoples R China

Along with Network Function Virtualization (NFV), Mobile Edge Computing (MEC) is becoming a new computing paradigm that enables accommodating innovative applications and services with stringent response delay and resource requirements, including autonomous vehicles and augmented reality. Provisioning reliable network services for users is the top priority of most network service providers, as unreliable services or severe service failures can result in tremendous losses of users, particularly for their mission-critical applications. In this paper, we study reliability-aware VNF instances provisioning in an MEC, where different users request different network services with different reliability requirements through paying their requested services with the aim to maximize the network throughput. To this end, we first formulate a novel reliability-aware VNF instance placement problem by provisioning primary and secondary VNF instances at different cloudlets in MEC for each user while meeting the specified reliability requirement of the user request. We then show that the problem is NP-hard and formulate an Integer Linear Programming (ILP) solution. Due to the NP-hardness of the problem, we instead devise an approximation algorithm with a logarithmic approximation ratio for the problem. Moreover, we also consider two special cases of the problem. For one special case where each request only requests one primary and one secondary VNF instances, the problem is still NP-hard, and we devise a constant approximation algorithm for it. For another special case where different VNFs have the same amounts of computing resource demands, we show that it is polynomial-time solvable by developing a dynamic programming solution for it. We finally evaluate the performance of the proposed algorithms through experimental simulations. Experimental results demonstrate that the proposed algorithms are promising, and the empirical results of the algorithms outperform their analytical counter

关键词： Reliability Cloud computing approximation algorithms Heuristic algorithms Mobile computing Edge computing Network function virtualization Reliability-aware VNF instances provisioning VNF instance placements virtualized network function implementation approximation algorithms generalized assignment problems dynamic programming mobile edge computing combinatorial optimization problems

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