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Gotowe bibliografie tematyczne / Distributed Selection Schemes

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Artykuły w czasopismach

Gotowa bibliografia na temat „Distributed Selection Schemes”

Autor: Grafiati

Data publikacji: 6 września 2023

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Artykuły w czasopismach na temat "Distributed Selection Schemes"

1

WEI, DAVID S. L., SANGUTHEVAR RAJASEKARAN, KSHIRASAGAR NAIK, and SY-YEN KUO. "EFFICIENT ALGORITHMS FOR SELECTION AND SORTING OF LARGE DISTRIBUTED FILES ON DE BRUIJN AND HYPERCUBE STRUCTURES." International Journal of Foundations of Computer Science 14, no. 06 (2003): 1129–46. http://dx.doi.org/10.1142/s0129054103002229.

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In this paper we show the power of sampling techniques in designing efficient distributed algorithms. In particular, we apply sampling techniques in the design of selection algorithms on the hypercube and de Bruijn networks, and show that the message complexity of selecting an item from a set (file) is less sensitive to the cardinality of the set (file). Given a file with n keys, our algorithm performs a selection on a p-node de Bruijn network or hypercube using only O(p log log n) messages and suffering a delay of O(τ log p log log n), with high probability. Our selection scheme outperforms t

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2

Zhang, Yang, Wei Wang, Xiangmo Zhao, and Jun Hou. "Distributed Antenna-and-Relay Selection Schemes for MIMO Cooperative Relay Network." Wireless Communications and Mobile Computing 2022 (July 13, 2022): 1–12. http://dx.doi.org/10.1155/2022/8729079.

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Relay selection, antenna selection, and link selection are three potent means to enrich network capacity in MIMO relaying networks. To reduce feedback overhead, in this paper, three distributed antenna-and-relay selection schemes are proposed for AF multiple-relay network, which is equipped with multiple antennas at nodes. Closed-form formulations of system outage probability are derived for these schemes, as well as the lower bounds and upper bounds. Furthermore, the approximate expressions of outage probability at high SNR regime are also derived, showing that the proposed optimal strategy c

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3

Wen, Yong-Kui, and Li-Min Sun. "Distributed ATMD for Buffeting Control of Cable-Stayed Bridges Under Construction." International Journal of Structural Stability and Dynamics 15, no. 03 (2015): 1450054. http://dx.doi.org/10.1142/s0219455414500540.

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This paper is concerned with mitigating multimode buffeting of cable-stayed bridges by optimizing the placements of active tuned mass dampers (ATMDs) and sensors and developing a control model and schemes. The Third Nanjing Bridge over the Yangtze River was used to formulate a mathematical control model with distributed ATMDs under wind action. Hankel norms were combined with structural mode analysis to build placement indices of the ATMDs and sensors under a defined objective while considering the influence of exterior excitation. A selection index of modes was proposed. ATMD/sensor placement

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4

Etezadi, Farrokh, Keyvan Zarifi, Ali Ghrayeb, and Sofiene Affes. "Decentralized Relay Selection Schemes in Uniformly Distributed Wireless Sensor Networks." IEEE Transactions on Wireless Communications 11, no. 3 (2012): 938–51. http://dx.doi.org/10.1109/twc.2012.010312.101314.

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5

Salama, Ahmed M., Islam Samy, Ahmed El Shafie, Amr Mohamed, and Tamer Khattab. "Centralized and Distributed Cognitive Relay-Selection Schemes for SWIPT Cognitive Networks." IEEE Transactions on Communications 67, no. 11 (2019): 7431–43. http://dx.doi.org/10.1109/tcomm.2019.2936562.

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6

Lee, Jaehwan, Hyeonseong Choi, Hyeonwoo Jeong, Baekhyeon Noh, and Ji Sun Shin. "Communication Optimization Schemes for Accelerating Distributed Deep Learning Systems." Applied Sciences 10, no. 24 (2020): 8846. http://dx.doi.org/10.3390/app10248846.

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In a distributed deep learning system, a parameter server and workers must communicate to exchange gradients and parameters, and the communication cost increases as the number of workers increases. This paper presents a communication data optimization scheme to mitigate the decrease in throughput due to communication performance bottlenecks in distributed deep learning. To optimize communication, we propose two methods. The first is a layer dropping scheme to reduce communication data. The layer dropping scheme we propose compares the representative values of each hidden layer with a threshold

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7

Park, Jinho, and Kwangsue Chung. "Distributed DRL-Based Computation Offloading Scheme for Improving QoE in Edge Computing Environments." Sensors 23, no. 8 (2023): 4166. http://dx.doi.org/10.3390/s23084166.

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Various edge collaboration schemes that rely on reinforcement learning (RL) have been proposed to improve the quality of experience (QoE). Deep RL (DRL) maximizes cumulative rewards through large-scale exploration and exploitation. However, the existing DRL schemes do not consider the temporal states using a fully connected layer. Moreover, they learn the offloading policy regardless of the importance of experience. They also do not learn enough because of their limited experiences in distributed environments. To solve these problems, we proposed a distributed DRL-based computation offloading

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8

Ge, L., G. J. Chen, and J. A. Chambers. "Relay Selection in Distributed Transmission Based on the Golden Code Using ML and Sphere Decoding in Wireless Networks." International Journal of Information Technology and Web Engineering 6, no. 4 (2011): 63–75. http://dx.doi.org/10.4018/jitwe.2011100105.

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The implementation of cooperative diversity with relays has advantages over point-to-point multiple-input multiple-output (MIMO) systems, in particular, overcoming correlated paths due to small inter-element spacing. A simple transmitter with one antenna may exploit cooperative diversity or space time coding gain through distributed relays. In this paper, similar distributed transmission is considered with the golden code, and the authors propose a new strategy for relay selection, called the maximum-mean selection policy, for distributed transmission with the full maximum-likelihood (ML) deco

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9

Ma, Bo, Jianhua Ge, Weiliang Han, and Jing Li. "Auction-Based Pricing Schemes for Distributed Partner Selection in Cooperative Wireless Networks." Wireless Personal Communications 96, no. 1 (2017): 265–90. http://dx.doi.org/10.1007/s11277-017-4166-7.

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10

Wei, David S. L., Sanguthevar Rajasekaran, Z. Cheng, K. Naik, and Sy-Yen Kuo. "Efficient Selection and Sorting Schemes Using Coteries for Processing Large Distributed Files." Journal of Parallel and Distributed Computing 62, no. 8 (2002): 1295–313. http://dx.doi.org/10.1006/jpdc.2002.1861.

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