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Journal articles on the topic 'Dense networks'

Author: Grafiati

Published: 25 January 2025

Last updated: 19 July 2025

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1

SIPPER, MOSHE. "CLUSTER-DENSE NETWORKS." International Journal of Modern Physics C 19, no. 06 (2008): 939–46. http://dx.doi.org/10.1142/s0129183108012650.

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Small-world networks, exhibiting short nodal distances and high clustering, and scale-free networks, typified by a scale-free, power-law node-degree distribution, have been shown to be widespread both in natural and artificial systems. We propose a new type of network — cluster-dense network — characterized by multiple clusters that are highly intra-connected and sparsely inter-connected. Employing two graph-theoretic measures — local density and relative density — we demonstrate that such networks are prevalent in the world of networks.

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2

Campbell, Lowell. "Dense group networks." Discrete Applied Mathematics 37-38 (July 1992): 65–71. http://dx.doi.org/10.1016/0166-218x(92)90125-t.

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3

Wang, Wei, Yutao Li, Ting Zou, Xin Wang, Jieyu You, and Yanhong Luo. "A Novel Image Classification Approach via Dense-MobileNet Models." Mobile Information Systems 2020 (January 6, 2020): 1–8. http://dx.doi.org/10.1155/2020/7602384.

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As a lightweight deep neural network, MobileNet has fewer parameters and higher classification accuracy. In order to further reduce the number of network parameters and improve the classification accuracy, dense blocks that are proposed in DenseNets are introduced into MobileNet. In Dense-MobileNet models, convolution layers with the same size of input feature maps in MobileNet models are taken as dense blocks, and dense connections are carried out within the dense blocks. The new network structure can make full use of the output feature maps generated by the previous convolution layers in dense blocks, so as to generate a large number of feature maps with fewer convolution cores and repeatedly use the features. By setting a small growth rate, the network further reduces the parameters and the computation cost. Two Dense-MobileNet models, Dense1-MobileNet and Dense2-MobileNet, are designed. Experiments show that Dense2-MobileNet can achieve higher recognition accuracy than MobileNet, while only with fewer parameters and computation cost.

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4

Athanasiadou, Georgia E., Panagiotis Fytampanis, Dimitra A. Zarbouti, George V. Tsoulos, Panagiotis K. Gkonis, and Dimitra I. Kaklamani. "Radio Network Planning towards 5G mmWave Standalone Small-Cell Architectures." Electronics 9, no. 2 (2020): 339. http://dx.doi.org/10.3390/electronics9020339.

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The 5G radio networks have introduced major changes in terms of service requirements and bandwidth allocation compared to cellular networks to date and hence, they have made the fundamental radio planning problem even more complex. In this work, the focus is on providing a generic analysis for this problem with the help of a proper multi-objective optimization algorithm that considers the main constraints of coverage, capacity and cost for high-capacity scenarios that range from dense to ultra-dense mmWave 5G standalone small-cell network deployments. The results produced based on the above analysis demonstrate that the denser the small-cell deployment, the higher the area throughput, and that a sectored microcell configuration can double the throughput for ultra-dense networks compared to dense networks. Furthermore, dense 5G networks can actually have cell radii below 400 m and down to 120 m for the ultra-dense sectored network that also reached spectral efficiency 9.5 bps/Hz/Km2 with no MIMO or beamforming.

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5

Zou, Kingsley Jun, and Kristo Wenjie Yang. "Network synchronization for dense small cell networks." IEEE Wireless Communications 22, no. 2 (2015): 108–17. http://dx.doi.org/10.1109/mwc.2015.7096293.

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6

Ge, Xiaohu, Song Tu, Guoqiang Mao, Cheng-Xiang Wang, and Tao Han. "5G Ultra-Dense Cellular Networks." IEEE Wireless Communications 23, no. 1 (2016): 72–79. http://dx.doi.org/10.1109/mwc.2016.7422408.

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7

F., Aguiló, F., E. E., Simó, and M. M., Zaragozá. "On Dense Triple-Loop Networks." Electronic Notes in Discrete Mathematics 10 (November 2001): 261–64. http://dx.doi.org/10.1016/s1571-0653(04)00406-8.

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8

Roy, Saptarshi, Titas Chanda, Tamoghna Das, Aditi Sen(De), and Ujjwal Sen. "Deterministic quantum dense coding networks." Physics Letters A 382, no. 26 (2018): 1709–15. http://dx.doi.org/10.1016/j.physleta.2018.04.033.

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9

Kamel, Mahmoud, Walaa Hamouda, and Amr Youssef. "Ultra-Dense Networks: A Survey." IEEE Communications Surveys & Tutorials 18, no. 4 (2016): 2522–45. http://dx.doi.org/10.1109/comst.2016.2571730.

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10

Oyakhire, Omuwa, and Koichi Gyoda. "Improved Proactive Routing Protocol Considering Node Density Using Game Theory in Dense Networks." Future Internet 12, no. 3 (2020): 47. http://dx.doi.org/10.3390/fi12030047.

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In mobile ad hoc networks, network nodes cooperate by packet forwarding from the source to the destination. As the networks become denser, more control packets are forwarded, thus consuming more bandwidth and may cause packet loss. Recently, game theory has been applied to address several problems in mobile ad hoc networks like energy efficiency. In this paper, we apply game theory to reduce the control packets in dense networks. We choose a proactive routing protocol, Optimized Link State Routing (OLSR) protocol. We consider two strategies in this method: willingness_always and willingness_never to reduce the multipoint relay (MPR) ratio in dense networks. Thus, nodes with less influence on other nodes are excluded from nomination as MPRs. Simulations were used to confirm the efficiency of using our improved method. The results show that the MPR ratio was significantly reduced, and packet delivery ratio was increased compared to the conventional protocol.

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11

Koucheryavy, Andrey, Alexander Paramonov, Mariya Makolkina, et al. "3 Dimension Multilayer Heterogenous Ultra Dense Networks." Telecom IT 10, no. 3 (2022): 1–12. http://dx.doi.org/10.31854/2307-1303-2022-10-3-1-12.

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The development of communication networks poses new challenges in the field of scientific research. At the same time, one of the main directions of development is the creation of highdensity and ultra-dense networks. Ultra-dense networks already belong to the technologies of communication networks of the sixth generation and the requirements for them are formed in the conditions of their deployment in three-dimensional space. Starting with the construction of fifth generation communication networks, communication networks are considered as heterogeneous, in which various technologies can be used together in the process of providing network services, for example, the Internet of Things, unmanned aerial vehicles, vehicular ad hoc networks, etc. This leads to the need to define and study three-dimensional multi-layer heterogeneous ultra-dense networks, which is the subject of this article.

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12

Koudouridis, Georgios P., and Pablo Soldati. "Spectrum and Network Density Management in 5G Ultra-Dense Networks." IEEE Wireless Communications 24, no. 5 (2017): 30–37. http://dx.doi.org/10.1109/mwc.2017.1700087.

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13

Sun, Kun, Xianbin Wen, Liming Yuan, and Haixia Xu. "Dense capsule networks with fewer parameters." Soft Computing 25, no. 10 (2021): 6927–45. http://dx.doi.org/10.1007/s00500-021-05774-6.

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14

Hao, Yixue, Min Chen, Long Hu, Jeungeun Song, Mojca Volk, and Iztok Humar. "Wireless Fractal Ultra-Dense Cellular Networks." Sensors 17, no. 4 (2017): 841. http://dx.doi.org/10.3390/s17040841.

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15

Al-Dulaimi, Anwer, Saba Al-Rubaye, John Cosmas, and Alagan Anpalagan. "Planning of Ultra-Dense Wireless Networks." IEEE Network 31, no. 2 (2017): 90–96. http://dx.doi.org/10.1109/mnet.2017.1500258nm.

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16

Cicconetti, Claudio, Antonio La Oliva, David Chieng, and Juan Zúñiga. "Extremely dense wireless networks [Guest Editorial]." IEEE Communications Magazine 53, no. 1 (2015): 88–89. http://dx.doi.org/10.1109/mcom.2015.7010520.

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17

Soret, Beatriz, Klaus I. Pedersen, Niels T. K. Jørgensen, and Víctor Fernández-López. "Interference coordination for dense wireless networks." IEEE Communications Magazine 53, no. 1 (2015): 102–9. http://dx.doi.org/10.1109/mcom.2015.7010522.

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18

Zhang, Haijun, Chunxiao Jiang, Mehdi Bennis, Merouane Debbah, Zhu Han, and Victor C. M. Leung. "Heterogeneous Ultra-Dense Networks: Part 1." IEEE Communications Magazine 55, no. 12 (2017): 68–69. http://dx.doi.org/10.1109/mcom.2017.8198804.

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19

Chen, Jun, Hongcheng Zhuang, and Zezhou Luo. "Energy Optimization in Dense OFDM Networks." IEEE Communications Letters 20, no. 1 (2016): 189–92. http://dx.doi.org/10.1109/lcomm.2015.2500584.

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20

Kartun-Giles, Alexander, Suhanya Jayaprakasam, and Sunwoo Kim. "Euclidean Matchings in Ultra-Dense Networks." IEEE Communications Letters 22, no. 6 (2018): 1216–19. http://dx.doi.org/10.1109/lcomm.2018.2799207.

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21

Han, Chaoyi, Yiping Duan, Xiaoming Tao, and Jianhua Lu. "Dense Convolutional Networks for Semantic Segmentation." IEEE Access 7 (2019): 43369–82. http://dx.doi.org/10.1109/access.2019.2908685.

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22

Zhang, Haijun, Chunxiao Jiang, Mehdi Bennis, Merouane Debbah, Zhu Han, and Victor C. M. Leung. "Heterogeneous Ultra Dense Networks: Part 2." IEEE Communications Magazine 56, no. 6 (2018): 12–13. http://dx.doi.org/10.1109/mcom.2018.8387196.

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23

Al-Dulaimi, Anwer, Qiang Ni, Junwei Cao, Alan Gatherer, and Chih-Lin I. "Orchestration of Ultra-Dense 5G Networks." IEEE Communications Magazine 56, no. 8 (2018): 68–69. http://dx.doi.org/10.1109/mcom.2018.8436048.

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24

Niesen, Urs. "Interference Alignment in Dense Wireless Networks." IEEE Transactions on Information Theory 57, no. 5 (2011): 2889–901. http://dx.doi.org/10.1109/tit.2011.2119690.

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25

Schuller, D. J., A. R. Rao, and G. D. Jeong. "Fractal characteristics of dense stream networks." Journal of Hydrology 243, no. 1-2 (2001): 1–16. http://dx.doi.org/10.1016/s0022-1694(00)00395-4.

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26

Arias-Castro, Ery, and Nicolas Verzelen. "Community detection in dense random networks." Annals of Statistics 42, no. 3 (2014): 940–69. http://dx.doi.org/10.1214/14-aos1208.

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27

Tsuda, Koji, and Elisabeth Georgii. "Dense module enumeration in biological networks." Journal of Physics: Conference Series 197 (December 1, 2009): 012012. http://dx.doi.org/10.1088/1742-6596/197/1/012012.

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28

Balderas, Luis, Miguel Lastra, and José M. Benítez. "Optimizing dense feed-forward neural networks." Neural Networks 171 (March 2024): 229–41. http://dx.doi.org/10.1016/j.neunet.2023.12.015.

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29

Yang, Bin, Guoqiang Mao, Ming Ding, Xiaohu Ge, and Xiaofeng Tao. "Dense Small Cell Networks: From Noise-Limited to Dense Interference-Limited." IEEE Transactions on Vehicular Technology 67, no. 5 (2018): 4262–77. http://dx.doi.org/10.1109/tvt.2018.2794452.

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30

Bhowmick, Sourav S. "How Connected Are Our Conference Review Boards?" ACM SIGMOD Record 51, no. 4 (2023): 74–78. http://dx.doi.org/10.1145/3582302.3582324.

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Dense co-authorship network formed by the review board members of a conference may adversely impact the quality and integrity of the review process. In this report, we shed light on the topological characteristics of such networks for three major data management conference venues. Our results show all these venues give rise to dense networks with a large giant component. We advocate to rethink the traditional way review boards are formed to mitigate the emergence of dense networks.

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31

Koudouridis, Georgios P., and Pablo Soldati. "Trading off Network Density with Frequency Spectrum for Resource Optimization in 5G Ultra-Dense Networks." Technologies 6, no. 4 (2018): 114. http://dx.doi.org/10.3390/technologies6040114.

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To effectively increase the capacity in 5G wireless networks requires more spectrum and denser network deployments. However, due to the increasing network density, the coordination of network and spectrum management becomes a challenging task both within a single operator’s network and among multiple operators’ networks. In this article, we develop new radio resource management (RRM) algorithms for adapting the frequency spectrum and the density of active access nodes in 5G ultra-dense networks (UDNs) to the traffic load and the user density in different geographical areas of the network. To this end, we formulate a network optimization problem where the allocation of spectrum bandwidth and the density of active access nodes are optimized to minimize a joint cost function, and we exploit Lagrange duality techniques to develop provably optimal network-scheduling algorithms. In particular, we develop density algorithms for two application scenarios. The first scenario solves the resource management problem for an operator of an ultra-dense network with exclusive access to a pool of frequency resources, while the second scenario applies to the management of the network density of collocated UDNs that belong to multiple operators sharing the same frequency spectrum. Simulation results demonstrate how effectively the algorithms can adapt the allocation of the spectrum allocation and the density of active access nodes over space and time.

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32

Kim, Seungnyun, Junwon Son, and Byonghyo Shim. "Energy-Efficient Ultra-Dense Network Using LSTM-based Deep Neural Networks." IEEE Transactions on Wireless Communications 20, no. 7 (2021): 4702–15. http://dx.doi.org/10.1109/twc.2021.3061577.

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33

Liu, Junyu, Min Sheng, and Jiandong Li. "Improving Network Capacity Scaling Law in Ultra-Dense Small Cell Networks." IEEE Transactions on Wireless Communications 17, no. 9 (2018): 6218–30. http://dx.doi.org/10.1109/twc.2018.2856766.

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34

MATSUDA, T., T. NOGUCHI, and T. TAKINE. "Broadcasting with Randomized Network Coding in Dense Wireless Ad Hoc Networks." IEICE Transactions on Communications E91-B, no. 10 (2008): 3216–25. http://dx.doi.org/10.1093/ietcom/e91-b.10.3216.

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35

Gowda, V. Dankan, Avinash Sharma, S. Kumaraswamy, et al. "A novel approach of unsupervised feature selection using iterative shrinking and expansion algorithm." Journal of Interdisciplinary Mathematics 26, no. 3 (2023): 519–30. http://dx.doi.org/10.47974/jim-1678.

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An major constraint in the realm of feature selection is that users choose the ideal number of characteristics that must be picked. In this article, an effort is made to automate the process of determining a suitable value for the appropriate the quantity of characteristics that must be chosen for better recognition tasks. To estimate the ideal amount of features that should be maintained for properly describing the data, we use the dense subgraph discovery approach for this goal. Notably, the existing methods uses a similar kind of approach called the dense subgraph finding. But the earlier approach obtains a single dense subgraph, while the task of dense subgraphs finding obtains a number of dense subgraphs that are important for learning the internal structure of any network. Thus, dense subgraphs finding is likely to be adopted as a natural choice for realizing the complex relations among the nodes of massive real-life networks, such as biological networks, social networks.

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36

Diamantoulakis, Panagiotis D., Vasilis K. Papanikolaou, and George K. Karagiannidis. "Optimization of Ultra-Dense Wireless Powered Networks." Sensors 21, no. 7 (2021): 2390. http://dx.doi.org/10.3390/s21072390.

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The internet-of-things (IoT) is expected to have a transformative impact in several different domains, including energy management in smart grids, manufacturing, transportation, smart cities and communities, smart food and farming, and healthcare. To this direction, the maintenance cost of IoT deployments has been identified as one of the main challenges, which is directly related to energy efficiency and autonomy of IoT solutions. In order to increase the energy sustainability of next-generation IoT, wireless power transfer (WPT) emerged as a promising technology; however, its effectiveness is hindered as the distance between the base station and the wireless powered IoT devices increases. To counter this effect, decentralized approaches based on the use of distributed densely deployed remote radio heads (RRHs) can be utilized to diminish the distance between the transmitting and the receiving nodes. A trade-off ensues from the use of RRHs as power beacons (PBs) or access points (APs) that enable either energy transfer during downlink or information reception during uplink, respectively. To balance this trade-off, in this work, the maximization of the ergodic rate in ultra-dense wireless powered networks is investigated. In more detail, three different protocols are introduced, optimized, and compared to each other: density splitting, time splitting, and hybrid time and density splitting, which are based on the optimization of the portion of the number of RRHs that are employed as PBs or APs at a specific time instance. Additionally, two different policies are taken into account regarding the PBs’ power constraint. The formulated problems that correspond to the combination of the proposed protocols with each of the two considered power constraint policies are optimally solved by using convex optimization tools and closed-form solutions are derived that result to useful insights. Finally, numerical results are provided, which illustrate the ergodic rate achieved by each of the proposed protocols and offer interesting conclusions regarding their comparison, which are directly linked to design guidelines and the required capital and operational expenses.

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37

Hackl, M., R. Malservisi, and S. Wdowinski. "Strain rate patterns from dense GPS networks." Natural Hazards and Earth System Sciences 9, no. 4 (2009): 1177–87. http://dx.doi.org/10.5194/nhess-9-1177-2009.

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Abstract. The knowledge of the crustal strain rate tensor provides a description of geodynamic processes such as fault strain accumulation, which is an important parameter for seismic hazard assessment, as well as anthropogenic deformation. In the past two decades, the number of observations and the accuracy of satellite based geodetic measurements like GPS greatly increased, providing measured values of displacements and velocities of points. Here we present a method to obtain the full continuous strain rate tensor from dense GPS networks. The tensorial analysis provides different aspects of deformation, such as the maximum shear strain rate, including its direction, and the dilatation strain rate. These parameters are suitable to characterize the mechanism of the current deformation. Using the velocity fields provided by SCEC and UNAVCO, we were able to localize major active faults in Southern California and to characterize them in terms of faulting mechanism. We also show that the large seismic events that occurred recently in the study region highly contaminate the measured velocity field that appears to be strongly affected by transient postseismic deformation. Finally, we applied this method to coseismic displacement data of two earthquakes in Iceland, showing that the strain fields derived by these data provide important information on the location and the focal mechanism of the ruptures.

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38

MANO, Toru, Takeru INOUE, Kimihiro MIZUTANI, and Osamu AKASHI. "Reducing Dense Virtual Networks for Fast Embedding." IEICE Transactions on Communications E103.B, no. 4 (2020): 347–62. http://dx.doi.org/10.1587/transcom.2019nrp0004.

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39

Lam, Sinh Cong, and Xuan Nam Tran. "Fractional Frequency Reuse in Ultra Dense Networks." Physical Communication 48 (October 2021): 101433. http://dx.doi.org/10.1016/j.phycom.2021.101433.

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40

Kassabov, Martin, Steven H. Strogatz, and Alex Townsend. "Sufficiently dense Kuramoto networks are globally synchronizing." Chaos: An Interdisciplinary Journal of Nonlinear Science 31, no. 7 (2021): 073135. http://dx.doi.org/10.1063/5.0057659.

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41

Argyriou, Antonios, Konstantinos Poularakis, George Iosifidis, and Leandros Tassiulas. "Video Delivery in Dense 5G Cellular Networks." IEEE Network 31, no. 4 (2017): 28–34. http://dx.doi.org/10.1109/mnet.2017.1600298.

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42

Dandelski, Conrad, Bernd-ludwig Wenning, Daniel Perez, Dirk Pesch, and Jean-paul Linnartz. "Scalability of dense wireless lighting control networks." IEEE Communications Magazine 53, no. 1 (2015): 157–65. http://dx.doi.org/10.1109/mcom.2015.7010529.

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43

Baldemair, Robert, Tim Irnich, Kumar Balachandran, et al. "Ultra-dense networks in millimeter-wave frequencies." IEEE Communications Magazine 53, no. 1 (2015): 202–8. http://dx.doi.org/10.1109/mcom.2015.7010535.

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44

Lin, Michael, Simone Silvestri, Novella Bartolini, and Thomas F. La Porta. "On Selective Activation in Dense Femtocell Networks." IEEE Transactions on Wireless Communications 15, no. 10 (2016): 7018–29. http://dx.doi.org/10.1109/twc.2016.2594784.

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45

Lu, Jianfeng, and Stefan Steinerberger. "Synchronization of Kuramoto oscillators in dense networks." Nonlinearity 33, no. 11 (2020): 5905–18. http://dx.doi.org/10.1088/1361-6544/ab9baa.

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46

Zhong, Yi, Xiaohu Ge, Howard H. Yang, Tao Han, and Qiang Li. "Traffic Matching in 5G Ultra-Dense Networks." IEEE Communications Magazine 56, no. 8 (2018): 100–105. http://dx.doi.org/10.1109/mcom.2018.1700956.

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47

Zhang, Siwei, Emanuel Staudinger, Thomas Jost, et al. "Distributed Direct Localization Suitable for Dense Networks." IEEE Transactions on Aerospace and Electronic Systems 56, no. 2 (2020): 1209–27. http://dx.doi.org/10.1109/taes.2019.2928606.

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48

Angus, John C., and Frank Jansen. "Dense ‘‘diamondlike’’ hydrocarbons as random covalent networks." Journal of Vacuum Science & Technology A: Vacuum, Surfaces, and Films 6, no. 3 (1988): 1778–82. http://dx.doi.org/10.1116/1.575296.

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49

Stadler, Florian J., and Bing Du. "Dense bottlebrushes enable supersoft solvent-free networks." NPG Asia Materials 8, no. 6 (2016): e276-e276. http://dx.doi.org/10.1038/am.2016.69.

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50

Pósfai, M., A. Fekete, and G. Vattay. "Shortest-path sampling of dense homogeneous networks." EPL (Europhysics Letters) 89, no. 1 (2010): 18007. http://dx.doi.org/10.1209/0295-5075/89/18007.

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