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IEEE Wireless Communications and Networking Conference (WCNC)

作者： Huang, Liang Zhou, Yiqing Han, Xue Wang, Yuanyuan Qian, Manli Shi, Jinglin Chinese Acad Sci Inst Comp Technol Wireless Commun Technol Res Ctr Beijing 100190 Peoples R China

ISBN: (纸本)9781467359399;9781467359382

Small cell cluster is a new paradigm to extend the usage of small cells from residential environment to large indoor or outdoor areas. However, coverage optimization is a challenge due to the ad-hoc deployment and plug-and-play feature of small cells. This paper considers decentralized self-optimization network (SON) architecture of small cell cluster and proposes distributed coverage optimization algorithm using game theory (DGT). A non-cooperate game is modeled to tune the Tx power of each small cell with a net utility function considering both gain of throughput and punishment of interference. Nash Equilibrium (NE) is proved to be existed in the game and a power update scheme is proposed which converges to the NE. Simulation results show that DGT can significantly improve throughput as well as coverage ratio with only several iterations. Compared with centralized algorithm such as modified particle swarm optimization (MPSO) and simulated annealing (SA), DGT algorithm reaches higher network throughput, uses less iteration and keeps considerable coverage ratio.

关键词： Small Cell Small Cell Cluster coverage optimization Throughput coverage Ratio Game Theory

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Titul Research on Wireless Network coverage for Transformation and Upgrading of Exhibition Management with Artificial Intelligence Technology

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APPLIED MATHEMATICS AND NONLINEAR SCIENCES 2023年

作者： Dou, Peilin Zheng, Guohua Shanghai Int Studies Univ Xianda Coll Econ & Humanities Shanghai 200080 Peoples R China Shanghai Xunmeng Informat Technol Co Ltd Shanghai 200050 Peoples R China

The rapid development of artificial intelligence and modern communication technology provides a powerful opportunity to realize the transformation, upgrading and high-quality development of exhibition management. To solve the optimal coverage problem of wireless network in the exhibition area, a new generation of artificial intelligence algorithm is introduced to propose a novel method for solving the network coverage problem based on Harris Hawk optimization (HHO) algorithm. From the perspective of maximizing coverage and minimizing coverage redundancy, the multi-objective coverage optimization problem of wireless network nodes is optimally extracted into a function and solved using the improved HHO (IHHO) algorithm. Through the simulation of the wireless network coverage in the exhibition area, the experimental results show that the comprehensive evaluation index score of the optimization algorithm reaches 98.03%, which is higher than the traditional particle swarm optimizer (PSO) and the standard HHO. The proposed IHHO algorithm can be effectively applied to the network coverage problem and has a greater advantage over the traditional optimization methods in terms of improving node coverage, and has great practicality.

关键词： Keyword Artificial intelligence exhibition management wireless network coverage optimization Harris Hawk optimization algorithm

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Generation-based Evolutionary Tool for the optimization of Constellations (GenETOC)

Generation-based Evolutionary Tool for the Optimization of C...

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IEEE Aerospace Conference (AeroConf)

作者： Carden, Joshua Deacon, Shaun Kessler, Paul Speth, Paul Analyt Mech Associates Inc 21 Enterprise Pkwy Suite 300 Hampton VA 23666 USA 1 N Dryden St Hampton VA 23681 USA

ISBN: (纸本)9781728174365

With the rapid growth in the capabilities of smaller satellites, satellite architectures that replace a single, extremely capable spacecraft with multiple, cheaper ones are gaining in popularity. Unfortunately, the orbit design process for constellations can be significantly more involved, especially when the relative placement of the individual spacecraft within the constellation is not constrained by mission and/or science objectives. Optimizing a satellite constellation in the presence of multiple, competing objectives is a highly complex problem to which many traditional mathematical optimization methods cannot be applied and few tools exist to help mission designers search for promising candidate mission designs. The Generation-based Evolutionary Tool for the optimization of Constellations (GenETOC) has been created to search for near-optimal constellation design options. GenETOC combines a modified version of the Non-dominated Sorting Genetic Algorithm II (NSGA II) with STK Components libraries (a 3rd *** package created by Analytical Graphics Inc.) to create a framework that enables a mission designer to generate a simulation that models the design problem and obtain a family of potential, near-optimal solutions that can be investigated more in detail. GenETOC was developed in C# using the .NET framework with Windows Presentation Foundation (WPF) serving as the framework from which to create the graphical user interface (GUI). GenETOC user inputs can be categorized into three major data components: definition of the problem (areas of interest, satellite decision parameters, and sensor configurations), definition of performance objectives, and specification of the genetic algorithm (GA) parameters. In the problem definition component, the user is prompted to define the areas of interest against which the performance metrics will be computed, define the sensor parameters and attach them to specific spacecraft, select which satellite orbital parameters wil

关键词： orbit constellations coverage optimization revisit optimization genetic algorithm

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coverage optimization for Defect-Tolerance Logic Mapping on Nanoelectronic Crossbar Architectures

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Journal of Computer Science & Technology 2012年第5期27卷 979-988页

作者：袁博李斌 Natural Inspired Computation and Applications Lab Joint USTC-Birmingham Research Institute in Intelligent Computation and Its Applications University of Science and Technology of China

Emerging nano-devices with the corresponding nano-architectures are expected to supplement or even replace conventional lithography-based CMOS integrated circuits, while, they are also facing the serious challenge of high defect rates. In this paper, a new weighted coverage is defined as one of the most important evaluation criteria of various defecttolerance logic mapping algorithms for nanoelectronic crossbar architectures functional design. This new criterion is proved by experiments that it can calculate the number of crossbar modules required by the given logic function more accurately than the previous one presented by Yellambalase et al. Based on the new criterion, a new effective mapping algorithm based on genetic algorithm （GA） is proposed. Compared with the state-of-the-art greedy mapping algorithm, the proposed algorithm shows pretty good effectiveness and robustness in experiments on testing problems of various scales and defect rates, and superior performances are observed on problems of large scales and high defect rates.

关键词： coverage optimization defect-tolerance nanoelectronic crossbar logic mapping evolutionary algorithm

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Improved whale optimization algorithm and its application in heterogeneous wireless sensor networks

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INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS 2021年第5期17卷

作者： Yue, Yinggao You, Hairong Wang, Shuxin Cao, Li Wenzhou Univ Oujiang Coll Wenzhou 325035 Zhejiang Peoples R China Hubei Univ Arts & Sci Comp Sch Xiangyang Peoples R China Northeastern Univ Sch Informat Sci & Engn Shenyang Peoples R China

Aiming at the problems of node redundancy and network cost increase in heterogeneous wireless sensor networks, this article proposes an improved whale optimization algorithm coverage optimization method. First, establish a mathematical model that balances node utilization, coverage, and energy consumption. Second, use the sine-cosine algorithm to improve the whale optimization algorithm and change the convergence factor of the original algorithm. The linear decrease is changed to the nonlinear decrease of the cosine form, which balances the global search and local search capabilities, and adds the inertial weight of the synchronous cosine form to improve the optimization accuracy and speed up the search speed. The improved whale optimization algorithm solves the heterogeneous wireless sensor network coverage optimization model and obtains the optimal coverage scheme. Simulation experiments show that the proposed method can effectively improve the network coverage effect, as well as the utilization rate of nodes, and reduce network cost consumption.

关键词： Heterogeneous wireless sensor networks whale optimization algorithm coverage optimization sine and cosine algorithm energy balance

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Deep Learning based Mobile Network Management for 5G and Beyond 15

Deep Learning based Mobile Network Management for 5G and Bey...

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15th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET)

作者： Maksymyuk, Taras Gazda, Juraj Ruzicka, Marek Slapak, Eugen Bugar, Gabriel Han, Longzhe Lviv Polytech Natl Univ Dept Telecommun Lvov Ukraine Tech Univ Kosice Dept Comp & Informat Kosice Slovakia Tech Univ Kosice Dept Elect & Multimedia Commun Kosice Slovakia Nanchang Inst Technol Jiangxi Prov Key Lab Water Informat Cooperat Sens Nanchang Jiangxi Peoples R China

ISBN: (纸本)9781728155661

Over the last decade the deployment paradigm of the mobile networks has shifted from complex hierarchical structures towards decoupling of the control and transmission planes. Such transformation enables software defined network configurations that allow improving the scalability and flexibility of the network. However, the process of network management is not trivial, considering the complexity of modern 5G infrastructure, and requires new advanced solutions. In this paper, we propose a new architecture with additional plane, which is responsible for intelligent knowledge generation and decision making. Proposed approach allows to train deep neural networks by using generative adversarial learning for the scenario with limited amount of real data. We study the performance of the proposed approach for the particular use case of small cells coverage optimization considering given spatial and temporal variation of the traffic demand. Simulation results show that proposed approach achieves quasi-optimal small cells coverage in terms of overall network throughput even in conditions when there is a lack of real world statistical data.

关键词： 5G AI GAN coverage optimization

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Multi-Sector Joint Beamforming for Pilot coverage optimization

Multi-Sector Joint Beamforming for Pilot Coverage Optimizati...

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International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Fan, Dongdong Wu, Zhouyun Huang, Aiping Shan, Hangguan Zhuang, Hongcheng Zhejiang Univ Inst Informat & Commun Engn Hangzhou 310027 Zhejiang Peoples R China China Telecom Technol Innovat Ctr Beijing 100031 Peoples R China Zhejiang Provincial Key Lab Informat Network Tech Hangzhou 310027 Zhejiang Peoples R China Huawei Technol Co Ltd Shenzhen 518129 Peoples R China

ISBN: (纸本)9781479903085

coverage optimization is an important task which directly affects the performance of cellular networks. The signal-to-interference and noise ratio (SINR) is a key metrics for evaluating coverage effect and its ability of resisting radio propagation condition variation. It is difficult to improve the ability of the pilot coverage of multiple sectors to resist environment variation simultaneously by using the existing methods. In this paper, a novel coverage optimization method based on multi-sector joint beamforming is proposed to maximize the minimum SINR of the sectors. An iterative algorithm is then developed to obtain antenna array excitation weights. Simulation results show that the performance of our algorithm is superior to that of the existing algorithm.

关键词： coverage optimization joint beamforming max min SINR iterative algorithm

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Quantifying Robotic Swarm coverage 15th

Quantifying Robotic Swarm Coverage

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15th International Conference on Informatics in Control, Automation and Robotics (ICINCO)

作者： Anderson, Brendon G. Loeser, Eva Gee, Marissa Ren, Fei Biswas, Swagata Turanova, Olga Haberland, Matt Bertozzi, Andrea L. Univ Calif Berkeley Dept Mech Engn Berkeley CA 94720 USA Univ Calif San Diego Dept Math San Diego CA 92093 USA Cornell Univ Ctr Appl Math Ithaca NY 14853 USA Univ Calif Berkeley Ind Engn & Operat Res Berkeley CA 94720 USA Univ Edinburgh Sch Math Edinburgh EH9 3FD Midlothian Scotland Inst Adv Study Sch Math Princeton NJ 08540 USA Cal Poly BioResource & Agr Engn San Luis Obispo CA 93407 USA Univ Calif Los Angeles Dept Math Los Angeles CA 90095 USA

ISBN: (纸本)9783030319939;9783030319922

In the field of swarm robotics, the design and implementation of spatial density control laws has received much attention, with less emphasis being placed on performance evaluation. This work fills that gap by introducing an error metric that provides a quantitative measure of coverage for use with any control scheme. The proposed error metric is continuously sensitive to changes in the swarm distribution, unlike commonly used discretization methods. We analyze the theoretical and computational properties of the error metric and propose two benchmarks to which error metric values can be compared. The first uses the realizable extrema of the error metric to compute the relative error of an observed swarm distribution. We also show that the error metric extrema can be used to help choose the swarm size and effective radius of each robot required to achieve a desired level of coverage. The second bench-mark compares the observed distribution of error metric values to the probability density function of the error metric when robot positions are randomly sampled from the target distribution. We demonstrate the utility of this benchmark in assessing the performance of stochastic control algorithms. We prove that the error metric obeys a central limit theorem, develop a streamlined method for performing computations, and place the standard statistical tests used here on a firm theoretical footing. We provide rigorous theoretical development, computational methodologies, numerical examples, and MATLAB code for both benchmarks.

关键词： Swarm robotics Multi-agent systems coverage optimization Central limit theorem

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Surface Gateway Placement optimization for Underwater Sensor Networks 28

Surface Gateway Placement Optimization for Underwater Sensor...

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28th Telecommunications Forum (TELFOR)

作者： Kahriman, Berkay Demirtas, Ali Murat Tavli, Bulent TOBB Univ Econ & Technol TR-06560 Ankara Turkey

ISBN: (纸本)9780738142432

Research on Underwater Sensor Networks (UWSNs) have been increasing drastically due to their significant potential for solving technically challenging problems. However, the challenging operating conditions such as underwater transmission delay, severe path loss, noise, and limited operating frequency render the realization of efficient UWSNs challenging. Surface Gateway (SG) deployment becomes one of the promising solutions to mitigate such challenges. In this paper, the optimal positioning of an SG to maximize the sensor coverage is investigated. The optimal location is compared with the benchmark location computed by averaging the location of underwater sensors. The coverage performances of solutions by using both of these approaches are comparatively investigated in terms of different numbers of underwater sensor nodes and transmission power levels.

关键词： coverage optimization Surface Gateway Deployment Mixed Integer Nonlinear Programming Underwater Sensor Network

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coverage optimization for Femtocell Clusters using Modified Particle Swarm optimization

Coverage Optimization for Femtocell Clusters using Modified ...

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IEEE International Conference on Communications (ICC)

作者： Huang, Liang Zhou, Yiqing Hu, Jinlong Han, Xue Shi, Jinlin Chinese Acad Sci Inst Comp Technol Wireless Commun Technol Res Ctr Beijing 100190 Peoples R China

ISBN: (纸本)9781457720536

coverage optimization is a main challenge for femtocell clusters which are considered to be a promising solution to provide seamless cellular coverage for large indoor areas. Although particle swarm optimization (PSO) can be employed to solve the coverage optimization of femtocell clusters, it reduces to single particle swarm optimization (SPSO) when only one femtocell cluster is considered and can only find the local optimum solution. In this paper, a modified PSO (MPSO) algorithm is proposed, which employs a heuristic power control scheme to guide the algorithm to search for the global optimum solution. Simulation results show that MPSO significantly outperforms SPSO and fixed power scheme with low complexity. Moreover, MPSO can converge rapidly and is suitable for online coverage optimization.

关键词： Femtocell Femtocell Cluster coverage optimization Particle Swarm optimization

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