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Journal articles on the topic 'Multichannel networks'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 January 2023

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1

Podlazov, Viktor Sergeevich. "Multichannel non-blocking system area network with direct channels." Program Systems: Theory and Applications 13, no. 4 (September 19, 2022): 47–76. http://dx.doi.org/10.25209/2079-3316-2022-13-4-47-76.

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An unblockable non-blocking self-routing network with direct channels has been developed, in which packet conflicts are resolved at the entrance to the network by the procedure of connecting sources to the first cascade of the network , providing a packet duality. Packets blocked during conflict resolution are retransmitted by sources with minimal delays. Within the network, the occurrence of conflicts is prevented by means of parallelization of the network itself. The network is designed in 2-, 4- and 8-stage versions with scaling of the number of channels from several hundred to many millions, keeping the same network performance. The network can provide 1- or 2-channel fault tolerance at the same link rate. The overhead cost of achieving these properties is higher complexity of the network, that becomes comparable to the complexity of the theoretical non-blocking Clos switch, which has no known practical implementation.The purpose of the proposed networks is photonic networks with routing information transmission in packet headers represented as one-time-use binary numbers. The proposed networks are made in an extended circuit basis, consisting of switches and separate multiplexers and demultiplexers. The paper presents the characteristics of the constructed networks with the specified method of presenting routing information.

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2

Peng-Jun, Wan. "TWDM multichannel lightwave hypercube networks." Theoretical Computer Science 194, no. 1-2 (March 1998): 123–36. http://dx.doi.org/10.1016/s0304-3975(97)00003-0.

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3

Ye, Qing, and Changhua Liu. "A Multichannel Data Fusion Method Based on Multiple Deep Belief Networks for Intelligent Fault Diagnosis of Main Reducer." Symmetry 12, no. 3 (March 23, 2020): 483. http://dx.doi.org/10.3390/sym12030483.

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Aiming at the problems of poor efficiency of the intelligent fault diagnosis method of the main reducer and the poor effectiveness of multichannel data fusion, this paper proposes a multichannel data fusion method based on deep belief networks and random forest fusion for fault diagnosis. Multiple deep belief networks (MDBNs) are constructed to obtain deep representative features from multiple modalities of multichannel data. Random forest can fuse deep representative features achieved from MDBNs to construct the model of multiple deep belief networks fusion (MDBNF). The proposed method is applied to fault diagnosis of the main reducer and evaluation of the performance. Multiple deep belief network model fusions (MD BN F) are constructed to improve the multichannel data fusion effect. Single sensory data, multichannel data, and two intelligent models based on support vector machine and deep belief networks are used as comparison in the experiments. The results indicate that the classification accuracy of the test set collected by sensor 1 and sensor 2 is 88.35% and 88.73%, respectively. The comparison results show that the method has good convergence. The data fusion of the proposed diagnostic model can effectively improve the correlation between the collected vibration signals and the failure mode, thereby improving the diagnostic performance by nearly 8%, representing improved diagnostic accuracy.

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4

Alahmadi, Hanan, and Fatma Boabdullah. "A Review of Multi-Channel Medium Access Control Protocols for Wireless Sensor Networks." European Journal of Engineering and Technology Research 6, no. 6 (September 18, 2021): 39–53. http://dx.doi.org/10.24018/ejeng.2021.6.6.2222.

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Wireless Sensor Networks (WSNs) are witnessing a momentum spread especially with the growth of the Internet of Things (IoT) paradigm. Indeed, WSNs are considered as the main enabling infrastructure for IoT networks. Nowadays, the emerging WSNs applications require not only long network lifespan but also considerably high data rate. Consequently, conceiving Multichannel MAC protocols that save the scarceenergy budget of sensor nodes while providing high network throughput is crucial for the emerging WSNs applications. In this paper, a thorough review of recent multichannel MAC protocols is provided along with a classification framework to deeply understand the design aspects for each protocol.

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5

Alahmadi, Hanan, and Fatma Boabdullah. "A Review of Multi-Channel Medium Access Control Protocols for Wireless Sensor Networks." European Journal of Engineering and Technology Research 6, no. 6 (September 18, 2021): 39–53. http://dx.doi.org/10.24018/ejers.2021.6.6.2222.

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Wireless Sensor Networks (WSNs) are witnessing a momentum spread especially with the growth of the Internet of Things (IoT) paradigm. Indeed, WSNs are considered as the main enabling infrastructure for IoT networks. Nowadays, the emerging WSNs applications require not only long network lifespan but also considerably high data rate. Consequently, conceiving Multichannel MAC protocols that save the scarceenergy budget of sensor nodes while providing high network throughput is crucial for the emerging WSNs applications. In this paper, a thorough review of recent multichannel MAC protocols is provided along with a classification framework to deeply understand the design aspects for each protocol.

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6

Kyasanur, P., Jungmin So, C. Chereddi, and N. H. Vaidya. "Multichannel mesh networks: challenges and protocols." IEEE Wireless Communications 13, no. 2 (April 2006): 30–36. http://dx.doi.org/10.1109/mwc.2006.1632478.

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7

Mckinley, P. K. "Multicast Communication in Staircase Multichannel Networks." Journal of Parallel and Distributed Computing 23, no. 1 (October 1994): 60–71. http://dx.doi.org/10.1006/jpdc.1994.1119.

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8

Opałka, Sławomir, Dominik Szajerman, and Adam Wojciechowski. "LSTM multichannel neural networks in mental task classification." COMPEL - The international journal for computation and mathematics in electrical and electronic engineering 38, no. 4 (July 1, 2019): 1204–13. http://dx.doi.org/10.1108/compel-10-2018-0429.

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Purpose The purpose of this paper is to apply recurrent neural networks (RNNs) and more specifically long-short term memory (LSTM)-based ones for mental task classification in terms of BCI systems. The authors have introduced novel LSTM-based multichannel architecture model which proved to be highly promising in other fields, yet was not used for mental tasks classification. Design/methodology/approach Validity of the multichannel LSTM-based solution was confronted with the results achieved by a non-multichannel state-of-the-art solutions on a well-recognized data set. Findings The results demonstrated evident advantage of the introduced method. The best of the provided variants outperformed most of the RNNs approaches and was comparable with the best state-of-the-art methods. Practical implications The approach presented in the manuscript enables more detailed investigation of the electroencephalography analysis methods, invaluable for BCI mental tasks classification. Originality/value The new approach to mental task classification, exploiting LSTM-based RNNs with multichannel architecture, operating on spatial features retrieving filters, has been adapted to mental tasks with noticeable results. To the best of the authors’ knowledge, such an approach was not present in the literature before.

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9

Kim, R. Y., Jin Jin, and Baochun Li. "Drizzle: Cooperative Symbol-Level Network Coding in Multichannel Wireless Networks." IEEE Transactions on Vehicular Technology 59, no. 3 (March 2010): 1415–32. http://dx.doi.org/10.1109/tvt.2010.2040095.

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10

Liu, Jie, and Li Zhu. "Joint Resource Allocation Optimization of Wireless Sensor Network Based on Edge Computing." Complexity 2021 (March 29, 2021): 1–11. http://dx.doi.org/10.1155/2021/5556651.

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Resource allocation has always been a key technology in wireless sensor networks (WSN), but most of the traditional resource allocation algorithms are based on single interface networks. The emergence and development of multi-interface and multichannel networks solve many bottleneck problems of single interface and single channel networks, it also brings new opportunities to the development of wireless sensor networks, but the multi-interface and multichannel technology not only improves the performance of wireless sensor networks but also brings great challenges to the resource allocation of wireless sensor networks. Edge computing changes the traditional centralized cloud computing processing method into a method that reduces computing storage capacity to the edge of the network and faces users and terminals. Realize the advantages of lower latency, higher bandwidth, and fast response. Therefore, this paper proposes a joint optimization algorithm of resource allocation based on edge computing. We establish a wireless sensor allocation model and then propose our algorithm model combined with the advantages of edge computing. Compared with the traditional allocation algorithm (PCOA, MCMH, and TDMA), it can further improve the resource utilization, reduce the network energy consumption, increase network capacity, and reduce the complexity of the schemes.

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11

Angelov, K., and S. Sadinov. "Modelling and Simulation Analysis of Routing Algorithms in Multichannel Optical Communication Networks." Journal of CIEES 1, no. 1 (June 3, 2021): 29–33. http://dx.doi.org/10.48149/jciees.2021.1.1.5.

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In this paper, it is considered the broadband backbone optical networks with wavelength routing and circuit switching used to build long-range wide area networks. In this type of network, if there is an available and acknowledged connection request, it is necessary to determine the optimal path between the optical communication nodes in the network. This also requires the assignment of an optimal set of wavelengths along the selected route between these nodes. This paper takes into account the multichannel optical communication networks with spectral multiplexing. Four different routing algorithms are modeled and analyzed for which their weight functions are determined to take into account various factors, such as the total distance of the individual routes, the total number of available wavelengths for a given route and how many of them are available for use. It is studied and compared the performance of the proposed algorithms in a multichannel optical network in terms of blocking probability.

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12

Tian, Xuejun, Tetsuo Ideguchi, and Takashi Okuda. "Multichannel Reservation Protocol in Ad-Hoc Networks." IEEJ Transactions on Electronics, Information and Systems 124, no. 1 (2004): 94–100. http://dx.doi.org/10.1541/ieejeiss.124.94.

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13

Olshansky, R., A. R. Bugos, and R. T. Hofmeister. "Multigigabit, Multichannel Lightwave Networks Using Subcarrier Multiplexing." Journal of High Speed Networks 2, no. 1 (1993): 63–79. http://dx.doi.org/10.3233/jhs-1993-2103.

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14

Kabacinski, W. "On nonblocking switching networks for multichannel connections." IEEE Transactions on Communications 43, no. 2/3/4 (February 1995): 222–24. http://dx.doi.org/10.1109/26.380039.

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15

Choi, Young-June, Suho Park, and Saewoong Bahk. "Multichannel random access in OFDMA wireless networks." IEEE Journal on Selected Areas in Communications 24, no. 3 (March 2006): 603–13. http://dx.doi.org/10.1109/jsac.2005.862422.

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16

Ramaswami, R., and P. A. Humblet. "Amplifier induced crosstalk in multichannel optical networks." Journal of Lightwave Technology 8, no. 12 (1990): 1882–96. http://dx.doi.org/10.1109/50.62886.

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17

Nasipuri, Asis, and Samir Ranjan Das. "Performance of multichannel wireless ad hoc networks." International Journal of Wireless and Mobile Computing 1, no. 3/4 (2006): 191. http://dx.doi.org/10.1504/ijwmc.2006.012555.

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18

Xue, Guoliang. "Optimal multichannel data transmission in computer networks." Computer Communications 26, no. 7 (May 2003): 759–65. http://dx.doi.org/10.1016/s0140-3664(02)00210-4.

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19

Wang, Xiuhui, Jiajia Zhang, and Wei Qi Yan. "Gait recognition using multichannel convolution neural networks." Neural Computing and Applications 32, no. 18 (October 19, 2019): 14275–85. http://dx.doi.org/10.1007/s00521-019-04524-y.

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20

Yang, D., J. Shin, and C. Kim. "Deterministic rendezvous scheme in multichannel access networks." Electronics Letters 46, no. 20 (2010): 1402. http://dx.doi.org/10.1049/el.2010.1990.

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21

Choi, S., and A. Cichocki. "Cascade neural networks for multichannel blind deconvolution." Electronics Letters 34, no. 12 (1998): 1186. http://dx.doi.org/10.1049/el:19980856.

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22

Campolo, Claudia, Antonella Molinaro, Alexey Vinel, and Yan Zhang. "Modeling Prioritized Broadcasting in Multichannel Vehicular Networks." IEEE Transactions on Vehicular Technology 61, no. 2 (February 2012): 687–701. http://dx.doi.org/10.1109/tvt.2011.2181440.

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23

Leung, V. C. M. "Packet reservation protocols for multichannel satellite networks." IEE Proceedings I Communications, Speech and Vision 140, no. 6 (1993): 453. http://dx.doi.org/10.1049/ip-i-2.1993.0066.

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24

Wu, Yafeng, Kin Sum Liu, John A. Stankovic, Tian He, and Shan Lin. "Efficient Multichannel Communications in Wireless Sensor Networks." ACM Transactions on Sensor Networks 12, no. 1 (March 21, 2016): 1–23. http://dx.doi.org/10.1145/2840808.

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25

Isazadeh, Ayaz, and Mohsen Heydarian. "Optimal multicast multichannel routing in computer networks." Computer Communications 31, no. 17 (November 2008): 4149–61. http://dx.doi.org/10.1016/j.comcom.2008.09.007.

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26

Marsan, Marco Ajmone, Pietro Camarda, and Fabio Neri. "Multichannel protocols for real-time microcomputer networks." Microprocessing and Microprogramming 18, no. 1-5 (December 1986): 469–77. http://dx.doi.org/10.1016/0165-6074(86)90080-3.

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27

Lebedev, Ye A. "Multichannel Stochastic Networks under Critical Load Conditions." Cybernetics and Systems Analysis 41, no. 1 (January 2005): 148–56. http://dx.doi.org/10.1007/s10559-005-0049-0.

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28

Lin, Kate Ching-Ju, and De-Nian Yang. "Multicast With Intraflow Network Coding in Multirate Multichannel Wireless Mesh Networks." IEEE Transactions on Vehicular Technology 62, no. 8 (October 2013): 3913–27. http://dx.doi.org/10.1109/tvt.2013.2260350.

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29

Cong, Qianhong, and Wenhui Lang. "Double Deep Recurrent Reinforcement Learning for Centralized Dynamic Multichannel Access." Wireless Communications and Mobile Computing 2021 (December 20, 2021): 1–10. http://dx.doi.org/10.1155/2021/5577756.

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We consider the problem of dynamic multichannel access for transmission maximization in multiuser wireless communication networks. The objective is to find a multiuser strategy that maximizes global channel utilization with a low collision in a centralized manner without any prior knowledge. Obtaining an optimal solution for centralized dynamic multichannel access is an extremely difficult problem due to the large-state and large-action space. To tackle this problem, we develop a centralized dynamic multichannel access framework based on double deep recurrent Q-network. The centralized node first maps current state directly to channel assignment actions, which can overcome prohibitive computation compared with reinforcement learning. Then, the centralized node can be easy to select multiple channels by maximizing the sum of value functions based on a trained neural network. Finally, the proposed method avoids collisions between secondary users through centralized allocation policy.

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30

Decina, M., V. Trecordi, G. Zanolini, and D. Zucca. "Broadcasting in multichannel MANs." Computer Communications 16, no. 2 (February 1993): 100–107. http://dx.doi.org/10.1016/0140-3664(93)90017-m.

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31

Gao, Zhong-Ke, Shan-Shan Zhang, Wei-Dong Dang, Shan Li, and Qing Cai. "Multilayer Network from Multivariate Time Series for Characterizing Nonlinear Flow Behavior." International Journal of Bifurcation and Chaos 27, no. 04 (April 2017): 1750059. http://dx.doi.org/10.1142/s0218127417500596.

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The exploration of two-phase flows, as a multidisciplinary subject, has drawn a great deal of attention on account of its significance. The dynamical flow behaviors underlying the transitions of oil–water bubbly flows are still elusive. We carry out oil–water two-phase flow experiments and capture multichannel flow information. Then we propose a novel methodology for inferring multilayer network from multivariate time series, which enables to fuse multichannel flow information at different frequency bands. We employ macro-scale, meso-scale and micro-scale network measures to characterize the generated multilayer networks, and the results suggest that our analysis allows uncovering the nonlinear flow behaviors underlying the transitions of oil-in-water bubbly flows.

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32

Ni, Qiufen, Chuanhe Huang, Panos M. Pardalos, Jia Ye, and Bin Fu. "Different Approximation Algorithms for Channel Scheduling in Wireless Networks." Mobile Information Systems 2020 (November 16, 2020): 1–13. http://dx.doi.org/10.1155/2020/8836517.

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We introduce a new two-side approximation method for the channel scheduling problem, which controls the accuracy of approximation in two sides by a pair of parameters f , g . We present a series of simple and practical-for-implementation greedy algorithms which give constant factor approximation in both sides. First, we propose four approximation algorithms for the weighted channel allocation problem: 1. a greedy algorithm for the multichannel with fixed interference radius scheduling problem is proposed and an one side O 1 -IS-approximation is obtained; 2. a greedy O 1 , O 1 -approximation algorithm for single channel with fixed interference radius scheduling problem is presented; 3. we improve the existing algorithm for the multichannel scheduling and show an E O d / ε time 1 − ϵ -approximation algorithm; 4. we speed up the polynomial time approximation scheme for single-channel scheduling through merging two algorithms and show a 1 − ϵ , O 1 -approximation algorithm. Next, we study two polynomial time constant factor greedy approximation algorithms for the unweighted channel allocation with variate interference radius. A greedy O 1 -approximation algorithm for the multichannel scheduling problem and an O 1 , O 1 -approximation algorithm for single-channel scheduling problem are developed. At last, we do some experiments to verify the effectiveness of our proposed methods.

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33

Lin, Ying Chih, Richard Chun-Hung Lin, and Tai-Wei Kuo. "Toward Maximizing Throughput in Multichannel Multirate Wireless Networks." IEEE Communications Letters 14, no. 8 (August 2010): 740–42. http://dx.doi.org/10.1109/lcomm.2010.08.100959.

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34

Zhang, Fan, and Yewen Cao. "Feasible routing selection mechanism in multichannel wireless networks." Electronics Letters 50, no. 5 (February 2014): 402–3. http://dx.doi.org/10.1049/el.2013.3461.

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35

Thomas, J., and E. Geraniotis. "Space-time iterative receivers for narrowband multichannel networks." IEEE Transactions on Communications 50, no. 7 (July 2002): 1049–54. http://dx.doi.org/10.1109/tcomm.2002.800825.

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36

Nugraha, Aditya Arie, Antoine Liutkus, and Emmanuel Vincent. "Multichannel Audio Source Separation With Deep Neural Networks." IEEE/ACM Transactions on Audio, Speech, and Language Processing 24, no. 9 (September 2016): 1652–64. http://dx.doi.org/10.1109/taslp.2016.2580946.

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37

Pavlov, A. V. "Asymptotically Optimal Queueing Disciplines for Some Multichannel Networks." Theory of Probability & Its Applications 36, no. 1 (January 1992): 185–90. http://dx.doi.org/10.1137/1136020.

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38

Zeng, Guokai, Bo Wang, Yong Ding, Li Xiao, and Matt Mutka. "Efficient Multicast Algorithms for Multichannel Wireless Mesh Networks." IEEE Transactions on Parallel and Distributed Systems 21, no. 1 (January 2010): 86–99. http://dx.doi.org/10.1109/tpds.2009.46.

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39

Fu, Luoyi, and Xinbing Wang. "Multicast Scaling Law in Multichannel Multiradio Wireless Networks." IEEE Transactions on Parallel and Distributed Systems 24, no. 12 (December 2013): 2418–28. http://dx.doi.org/10.1109/tpds.2012.334.

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40

Ji, C., J. J. Gronde, N. M. Maurits, and J. B. T. M. Roerdink. "Visual Exploration of Dynamic Multichannel EEG Coherence Networks." Computer Graphics Forum 38, no. 1 (November 5, 2018): 507–20. http://dx.doi.org/10.1111/cgf.13588.

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41

Chouikhi, Samira, Inès El Korbi, Yacine Ghamri-Doudane, and Leila Azouz Saidane. "Centralized connectivity restoration in multichannel wireless sensor networks." Journal of Network and Computer Applications 83 (April 2017): 111–23. http://dx.doi.org/10.1016/j.jnca.2017.01.040.

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42

Camarda, Pietro, and Massimo Cocolicchio. "Virtual topology design of multichannel multihop lightwave networks." Computer Communications 15, no. 7 (September 1992): 458–66. http://dx.doi.org/10.1016/0140-3664(92)90005-y.

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43

Hua, Cunqing, and Rong Zheng. "Robust Topology Engineering in Multiradio Multichannel Wireless Networks." IEEE Transactions on Mobile Computing 11, no. 3 (March 2012): 492–503. http://dx.doi.org/10.1109/tmc.2011.99.

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44

Chouikhi, Samira, Inès El Korbi, Yacine Ghamri-Doudane, and Leila Azouz Saidane. "Distributed connectivity restoration in multichannel wireless sensor networks." Computer Networks 127 (November 2017): 282–95. http://dx.doi.org/10.1016/j.comnet.2017.08.016.

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45

Ryoo, Sangjin. "Multichannel estimation algorithms for wideband OFDM-based networks." Contemporary Engineering Sciences 10 (2017): 631–39. http://dx.doi.org/10.12988/ces.2017.7875.

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46

Mohanty, B. P., and T. D. Todd. "Dynamic single-connection channel access in multichannel networks." IEE Proceedings E Computers and Digital Techniques 136, no. 1 (1989): 76. http://dx.doi.org/10.1049/ip-e.1989.0011.

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47

Weber, A., W. Fischer, and M. N. Huber. "Multichannel circuit switching-performance evaluation of switching networks." IEEE Journal on Selected Areas in Communications 9, no. 2 (1991): 226–32. http://dx.doi.org/10.1109/49.68451.

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48

Niranjan, Sugam Pandey, and Aura Ganz. "Design and Evaluation of Multichannel Multirate Wireless Networks." Mobile Networks and Applications 11, no. 5 (May 23, 2006): 697–709. http://dx.doi.org/10.1007/s11036-006-7796-7.

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49

Livinskaya, A. V., and E. A. Lebedev. "Limit theorem for multichannel networks in heavy traffic." Cybernetics and Systems Analysis 48, no. 6 (November 2012): 899–905. http://dx.doi.org/10.1007/s10559-012-9470-3.

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50

Park, Museong, and Wonsik Yoon. "Cross-layer Optimization for Multichannel Multiradio Multisession Wireless Networks with Network Coding." Journal of the Institute of Electronics Engineers of Korea 50, no. 5 (May 25, 2013): 18–24. http://dx.doi.org/10.5573/ieek.2013.50.5.018.

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