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Journal articles on the topic 'Cellular network'

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Published: 4 June 2021

Last updated: 1 June 2024

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1

Saravanan, K. "A Review on Green Cellular Network." International Journal of Trend in Scientific Research and Development Volume-2, Issue-5 (August 31, 2018): 342–49. http://dx.doi.org/10.31142/ijtsrd15839.

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2

Ohipeni, Edwin Angmor Kofi. "5G Cellular Network Forensics." Advances in Multidisciplinary and scientific Research Journal Publication 1, no. 1 (July 26, 2022): 181–84. http://dx.doi.org/10.22624/aims/crp-bk3-p29.

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The fifth generation (5G) of cellular networks will bring 10Gbps user speeds, have a wider bandwidth which can accommodate 1000 times what the fourth generation (4G) could and 100 times faster than the previous 4G cellular network. This paper’s focus is to highlight on the legal aspects of the 5G cellular network. That is, the health issues, privacy, and security issues on Africans. Most crimes currently are facilitated by cellular network devices and with the improvement that the 5G cellular network brings, there is going to be an increase in cybercrime and hence the need for forensics. These forensics’ techniques used in evidence acquisition violates the privacy and security of the users of the 5G cellular network. Keywords: Cellular Networks, Fifth Generation (5G), Lawful Interception (LI), Lawful Access Location Services (LALS), Privacy, Security, Africa, Natural Resources, Carbon, Ozone Layer, Internet of Things (IoT) BOOK Chapter ǀ Research Nexus in IT, Law, Cyber Security & Forensics. Open Access. Distributed Free Citation: Edwin Angmor Kofi Ohipeni (2022): 5G Cellular Network Forensics Book Chapter Series on Research Nexus in IT, Law, Cyber Security & Forensics. Pp 181-184 www.isteams.net/ITlawbookchapter2022. dx.doi.org/10.22624/AIMS/CRP-BK3-P29

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3

O. H. Abdelwahed, O. H. Abdelwahed, and M. El-Sayed Wahed. "Optimizing Single Layer Cellular Neural Network Simulator using Simulated Annealing Technique with Neural Networks." Indian Journal of Applied Research 3, no. 6 (October 1, 2011): 91–94. http://dx.doi.org/10.15373/2249555x/june2013/31.

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4

Fuster, Joaquin M. "Cellular Dynamics of Network Memory." Zeitschrift für Naturforschung C 53, no. 7-8 (August 1, 1998): 670–76. http://dx.doi.org/10.1515/znc-1998-7-819.

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Abstract One example of “emergence” is the development, as a result of neural ontogeny and living experience, of cortical networks capable of representing and retaining cognitive information. A large body of evidence from neuropsychology, electrophysiology and neuroimaging indicates that so-called working memory and long-term memory share the same neural substrate in the cerebral cortex. That substrate consists in a system of widespread, overlapping and hierarchically organized networks of cortical neurons. In this system, any neuron or group of neurons can be part of many networks, and thus many memories. Working memory is the temporary activation of one such network of long-term memory for the purpose of executing an action in the near future. The activation of the network may be brought about by stimuli that by virtue of prior experience are in some manner associated with the cognitive content of the network, including the response of the organism to those stimuli. The mechanisms by which the network stays activated are presumed to include the recurrent re-entry of impulses through associated neuronal assemblies of the network. Consistent with this notion is the following evidence: (1) working memory depends on the functional integrity of cortico-corti-cal connective loops; and (2) during working memory, remarkable similarities -including “attractor behavior” -have been observed between firing patterns in real cortex and in an artificial recurrent network.

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5

Kethanan, M., M. Jeyaseelan, and R. Lathersan. "Service Quality and Subscriber Satisfaction of Cellular Network." International Journal of Science and Research (IJSR) 12, no. 12 (December 5, 2023): 637–40. http://dx.doi.org/10.21275/sr231128101617.

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6

Zhu, Jia, and Yulong Zou. "Cognitive Network Cooperation for Green Cellular Networks." IEEE Access 4 (2016): 849–57. http://dx.doi.org/10.1109/access.2016.2532600.

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7

Roska, TamÃ¡s, and Giovanni Pazienza. "Cellular neural network." Scholarpedia 4, no. 12 (2009): 1519. http://dx.doi.org/10.4249/scholarpedia.1519.

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8

Tadeusiewicz, Ryszard. "Cellular neural networks (with CNNS [Cellular Neural Network Simulator] software — ver.3.6)." Neurocomputing 8, no. 2 (July 1995): 224–25. http://dx.doi.org/10.1016/0925-2312(95)90034-9.

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9

SHIN, Won-Yong, and Bang Chul JUNG. "Network Coordinated Opportunistic Beamforming in Downlink Cellular Networks." IEICE Transactions on Communications E95-B, no. 4 (2012): 1393–96. http://dx.doi.org/10.1587/transcom.e95.b.1393.

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10

Elsawy, Hesham, Hayssam Dahrouj, Tareq Y. Al-naffouri, and Mohamed-slim Alouini. "Virtualized cognitive network architecture for 5G cellular networks." IEEE Communications Magazine 53, no. 7 (July 2015): 78–85. http://dx.doi.org/10.1109/mcom.2015.7158269.

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11

Nussinov, Ruth, Chung-Jung Tsai, and Peter Csermely. "Allo-network drugs: harnessing allostery in cellular networks." Trends in Pharmacological Sciences 32, no. 12 (December 2011): 686–93. http://dx.doi.org/10.1016/j.tips.2011.08.004.

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12

Azari, Amin, and Guowang Miao. "Network Lifetime Maximization for Cellular-Based M2M Networks." IEEE Access 5 (2017): 18927–40. http://dx.doi.org/10.1109/access.2017.2753283.

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13

HAENGGI, MARTIN. "DISTRIBUTED SENSOR NETWORKS: A CELLULAR NONLINEAR NETWORK PERSPECTIVE." International Journal of Neural Systems 13, no. 06 (December 2003): 405–14. http://dx.doi.org/10.1142/s0129065703001686.

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Large-scale networks of integrated wireless sensors become increasingly tractable. Advances in hardware technology and engineering design have led to dramatic reductions in size, power consumption, and cost for digital circuitry, and wireless communications. Networking, self-organization, and distributed operation are crucial ingredients to harness the sensing, computing, and computational capabilities of the nodes into a complete system. This article shows that those networks can be considered as cellular nonlinear networks (CNNs), and that their analysis and design may greatly benefit from the rich theoretical results available for CNNs.

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14

Gamst, A., E. G. Zinn, R. Beck, and R. Simon. "Cellular Radio Network Planning." IEEE Aerospace and Electronic Systems Magazine 1, no. 2 (February 1986): 8–11. http://dx.doi.org/10.1109/maes.1986.5005038.

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15

Khadim, Saima, Adnan Waqar, Aamir Zeb, Imran Khan, and Izhar Hussain. "Smart Cognitive Cellular Network." International Journal of Future Generation Communication and Networking 10, no. 12 (December 31, 2017): 23–34. http://dx.doi.org/10.14257/ijfgcn.2017.10.12.03.

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16

Slavova, Angela. "Dynamic properties of cellular neural networks." Journal of Applied Mathematics and Stochastic Analysis 6, no. 2 (January 1, 1993): 107–16. http://dx.doi.org/10.1155/s1048953393000103.

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Dynamic behavior of a new class of information-processing systems called Cellular Neural Networks is investigated. In this paper we introduce a small parameter in the state equation of a cellular neural network and we seek for periodic phenomena. New approach is used for proving stability of a cellular neural network by constructing Lyapunov's majorizing equations. This algorithm is helpful for finding a map from initial continuous state space of a cellular neural network into discrete output. A comparison between cellular neural networks and cellular automata is made.

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17

Kanehisa, Minoru, Miho Furumichi, Yoko Sato, Mari Ishiguro-Watanabe, and Mao Tanabe. "KEGG: integrating viruses and cellular organisms." Nucleic Acids Research 49, no. D1 (October 30, 2020): D545—D551. http://dx.doi.org/10.1093/nar/gkaa970.

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Abstract KEGG (https://www.kegg.jp/) is a manually curated resource integrating eighteen databases categorized into systems, genomic, chemical and health information. It also provides KEGG mapping tools, which enable understanding of cellular and organism-level functions from genome sequences and other molecular datasets. KEGG mapping is a predictive method of reconstructing molecular network systems from molecular building blocks based on the concept of functional orthologs. Since the introduction of the KEGG NETWORK database, various diseases have been associated with network variants, which are perturbed molecular networks caused by human gene variants, viruses, other pathogens and environmental factors. The network variation maps are created as aligned sets of related networks showing, for example, how different viruses inhibit or activate specific cellular signaling pathways. The KEGG pathway maps are now integrated with network variation maps in the NETWORK database, as well as with conserved functional units of KEGG modules and reaction modules in the MODULE database. The KO database for functional orthologs continues to be improved and virus KOs are being expanded for better understanding of virus-cell interactions and for enabling prediction of viral perturbations.

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18

SHIMODAIRA, Hidekazu, Gia Khanh TRAN, Kei SAKAGUCHI, Kiyomichi ARAKI, Shinobu NANBA, and Satoshi KONISHI. "Diamond Cellular Network —Optimal Combination of Small Power Basestations and CoMP Cellular Networks —." IEICE Transactions on Communications E99.B, no. 4 (2016): 917–27. http://dx.doi.org/10.1587/transcom.2015ebp3224.

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19

Yue Ma, Yue Ma, Xin Li Yue Ma, and Jianbin Li Xin Li. "A Security Edge Computing Offloading Solution for 5G Cellular Network." 電腦學刊 32, no. 6 (December 2021): 015–23. http://dx.doi.org/10.53106/199115992021123206002.

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20

Chen-Wei Feng, Chen-Wei Feng, Xian-Guo Lu Chen-Wei Feng, Yu Sun Xian-Guo Lu, Huang-Bin Zeng Yu Sun, and Zhuo Li Huang-Bin Zeng. "Vehicle Clustering and Resource Allocation Algorithm Based on Cellular Network." 電腦學刊 34, no. 3 (June 2023): 031–47. http://dx.doi.org/10.53106/199115992023063403003.

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<p>As a special Mobile Ad-hoc Network (MANET), Vehicular Ad-hoc Network (VANET) plays a very important role in the future intelligent transportation system. In order to solve the problems of unstable communication connection, fast network topology change and low communication resource utilization caused by high vehicle mobility in VANET, a low-complexity resource allocation algorithm based on vehicle cluster is proposed. Firstly, considering the speed, position and moving direction of the vehicles, a vehicle clustering algorithm based on movement consistency is proposed to cluster the vehicles and keep the vehicle cluster stable. Secondly, a low-complexity resource allocation algorithm is proposed to improve the utilization rate of communication resources, which is constrained by the interference caused by the vehicle clusters to the cellular users. Simulation results show that the proposed algorithm has low complexity and can better maintain the stability of vehicle clusters and improve the system capacity in the common complex Internet of Vehicles (IoV) scenarios in cities.</p> <p> </p>

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21

Zhang, Xiaoxia, Zhongming Zheng, Qinghua Shen, Jing Liu, Xuemin Sherman Shen, and Liang-Liang Xie. "Optimizing Network Sustainability and Efficiency in Green Cellular Networks." IEEE Transactions on Wireless Communications 13, no. 2 (February 2014): 1129–39. http://dx.doi.org/10.1109/twc.2013.122713.131133.

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22

Shnaiwer, Yousef N., Sameh Sorour, Parastoo Sadeghi, Neda Aboutorab, and Tareq Y. Al-Naffouri. "Network-Coded Macrocell Offloading in Femtocaching-Assisted Cellular Networks." IEEE Transactions on Vehicular Technology 67, no. 3 (March 2018): 2644–59. http://dx.doi.org/10.1109/tvt.2017.2771416.

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23

Torrea-Duran, Rodolfo, Maximo Morales Cespedes, Jorge Plata-Chaves, Luc Vandendorpe, and Marc Moonen. "Topology-Aware Space-Time Network Coding in Cellular Networks." IEEE Access 6 (2018): 7565–78. http://dx.doi.org/10.1109/access.2017.2773709.

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24

Menachi, Eliav, and Ran Giladi. "Hierarchical Ethernet Transport Network Architecture for backhaul cellular networks." Wireless Networks 19, no. 8 (April 4, 2013): 1933–43. http://dx.doi.org/10.1007/s11276-013-0578-9.

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25

Lam, Patrick, and Soung Liew. "Nested Network Mobility on the Multihop Cellular Network." IEEE Communications Magazine 45, no. 9 (September 2007): 100–104. http://dx.doi.org/10.1109/mcom.2007.4342863.

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26

MawjoudA, Dr S. Abdul, and T. Hussien. "Capacity Enhancement InWCDMA Cellular Network." AL-Rafdain Engineering Journal (AREJ) 20, no. 1 (February 28, 2012): 110–22. http://dx.doi.org/10.33899/rengj.2012.47164.

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27

Pandey, Aunkita, Arindam Satpati, and Somnath Sardar. "BRAND LOYALTY FOR CELLULAR NETWORK." International Journal of Management, Public Policy and Research 1, no. 1 (March 31, 2022): 68–75. http://dx.doi.org/10.55829/ijmpr.v1i1.44.

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The Indian telecom industry is a very dynamic industry with very stiff competition amongst existing service providers in the telecom markets. But the brand loyalty among the customers gets difficult because of this competition. So, it gets necessary what consumers want and what can satisfy them. In that context the present papers are the research of 201 consumers as well as respondents of finding the attributes and factors for making the consumers loyal and stay connected to one brand only. The research was done by Google form in Gujarat state with the relevant literature review of the research doing presently.

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28

Gaur, Rajneesh K. "Cellular Network Based Drug Monitoring." Asia Pacific Journal of Public Health 23, no. 6 (November 2011): 1025–26. http://dx.doi.org/10.1177/1010539511422741.

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Pharmacovigilance is a resourceful process for monitoring adverse drug reactions. The lack of resources in developing countries makes it difficult to execute pharamcovigilance programs on a large scale. Therefore, the cellular technology based network, which has widespread access in the developing world, may be used as an inexpensive means of monitoring.

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29

Saha, D., and S. E. Kay. "Cellular digital packet data network." IEEE Transactions on Vehicular Technology 46, no. 3 (1997): 697–706. http://dx.doi.org/10.1109/25.618195.

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30

Jamal, A. M. M. "Neural Network and Cellular Manufacturing." Industrial Management & Data Systems 93, no. 3 (March 1993): 21–25. http://dx.doi.org/10.1108/02635579310027343.

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31

Kung, Jaeha, Duckhwan Kim, and Saibal Mukhopadhyay. "Adaptive Precision Cellular Nonlinear Network." IEEE Transactions on Very Large Scale Integration (VLSI) Systems 26, no. 5 (May 2018): 841–54. http://dx.doi.org/10.1109/tvlsi.2018.2794498.

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32

Kuusik, Alar, Takeo Hisada, Satoshi Suzuki, and Katsuhisa Furuta. "Cellular Network Telecontrolled Robot Vehicle." IFAC Proceedings Volumes 37, no. 7 (June 2004): 99–104. http://dx.doi.org/10.1016/s1474-6670(17)32131-6.

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33

Dahunsi, Folasade Mojisola, and Barry Dwolatzky. "Investigating Geometry-Aware Network-Based Positioning in Cellular Networks Using Neural Network Predictive Model." Wireless Personal Communications 90, no. 3 (June 16, 2016): 1413–32. http://dx.doi.org/10.1007/s11277-016-3401-y.

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34

M. Dhasaratham, Et al. "Big Data Network Optimization for Mobile Cellular Networks in 5G." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 10 (November 2, 2023): 1924–30. http://dx.doi.org/10.17762/ijritcc.v11i10.8783.

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5G ensures the provision of intelligent network and application services by means of connectivity to remote sensors, massive amounts of Internet of Things data, and fast data transmissions. Through the utilization of distributed compute architectures and by supporting massive connectivity across diverse devices like sensors, gateways, and controllers, 5G brings about a transformative revolution in the conversion of both big data at rest and data in motion into real-time intelligence. Big Data Analytics play an important role in the evolution of 5G standards, enabling intelligence across networks, applications, and businesses. Administrators of mobile organizations have access to a plethora of opportunities to enhance service quality through big data. Network optimization serves as a crucial method to achieve this task, with network prediction forming the foundation for such optimization. Ensuring network stability and security is essential for 5G mobile communication, considering its significance as an important tool in national life. Therefore, this work focuses on presenting big data network optimization for mobile cellular networks within the context of 5G. In order to improve the Quality of Experience (QoE) for users, this work explores various methods for integrating network optimization and Big Data analytics. The performance of the presented model is evaluated in terms of QoE, Throughput, handover rate, mobility, reliability, and network slicing.

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35

Hasan Al-Bowarab, Mustafa, Nurul Azma Zakaria, Zaheera Zainal Abidin, and Ziadoon Kamil Maseer. "Review on Device-to-Device Communication in Cellular based Network Systems." International Journal of Engineering & Technology 7, no. 3.20 (September 1, 2018): 435. http://dx.doi.org/10.14419/ijet.v7i3.20.20587.

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In a traditional cellular-network based system, the devices are not allowed to be interconnected directly in the certified mobile bandwidth, and all communications are carried out via the base station (BS). At present, device terminal broadcasting allows devices in the distributed network to act as transmitting relays to each other and reach a massive ad hoc network of networks that is different from the previous cellular architecture that faces technical challenges. Therefore, this article explores the application of a cellular-based two-layer network system that includes a base station (BS) cellular layer, such as cellular-to-device communication and communication between devices. In the proposed two-tier cellular-based network system, user data is transmitted through other users' devices to implement the privacy protection that is lacking in prior communication between devices in cellular systems. To ensure a negligible impact on the performance of current communication between devices, a two-layer network is assimilated to autonomous interference management schemes and associated resource allocation schemes. The findings from this review provide an overview of the major challenges in two-tier networks and propose a two-tier cellular-based system in which user data is routed through other users' devices to implement privacy protection.

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36

Liang, Bin, Mark A. Gregory, and Shuo Li. "Latency Analysis for Mobile Cellular Network uRLLC Services." Journal of Telecommunications and the Digital Economy 10, no. 3 (September 21, 2022): 39–57. http://dx.doi.org/10.18080/jtde.v10n3.447.

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The fifth generation (5G) mobile network technologies include ultra-Reliable Low Latency Communications (uRLLC) capability. To fully exploit uRLLC, distributed Multi- access Edge Computing (MEC) is being developed and introduced at the network edge with an architecture that supports applications and services. Some of the MEC applications will benefit from uRLLC, including virtual reality, augmented reality, education, health, online gaming, automatic manufacturing and Vehicle-to-everything. However, unique challenges and opportunities exist for 5G cellular networks and MEC due to a range of factors, including end-user device mobility and the implementation of the network Control Plane (CP) and User Plane (UP). In this regard, there is a need to optimize protocols and network architecture. This paper investigates latency and related network elements in the next generation mobile cellular network. We also analyze the 5G network latency in the CP and UP. Finally, the paper identifies protocol optimization considerations for MEC integration with 5G to achieve low end-to-end latency.

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37

Damanik, Hillman Akhyar. "Securing Data Network For Growing Business VPN architectures Cellular Network Connectivity." RSF Conference Series: Engineering and Technology 1, no. 2 (December 20, 2021): 14–20. http://dx.doi.org/10.31098/cset.v1i2.470.

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Private networks allow organizations to leverage, customize and dedicate LTE network capabilities to their own service needs. These networks are controlled and managed locally and can be optimized for specific network services and applications. Often they are used because there may be gaps in coverage where there is no cellular connectivity. Enterprises and businesses running on light application systems, using small bandwidth and requiring fast deployment such as ATMs, vending machines, digital signage kiosks and small store branches We propose a method and concept and implementation, in the form of a Data Network Security Configuration For Business Growth VPN architecture Mobile Network Connectivity for various businesses. By implementing products and services by developing the configuration and model of VPN Tunneling Protocol rules, using the EOIP Protocol and SSTP Protocol methods and virtualization schemes using the VLAN Bridging method on Wide Area Network (WAN) network connectivity. This method takes advantage of the LTE features found on the RB751U-2hnd and then integrates Huawei Mobile Broadband LTE. We also present a general Service Level Agreement (SLA) and an open source tools-based SLA network system Zabbix. Then configure the VPN Tunneling Protocol and its features on the RB751U-2hnd using Paramiko Network Automation. The focus of the results of this research is that by utilizing the available tools we will build a VPN with system monitoring facilities with the results achieved are network performance and availability and show that the design we build can be used for private connections for growing businesses. the results obtained from testing for 1 month for the 3 providers used, for the average throughput value with VPN tunneling applied TSEL 14.2 Mbps, ISAT 13.9 Mbps and XL 13.5 Mbps. The SLA value obtained is based on acceptable criteria, TSEL 91.5%, ISAT 91.8% and XL 90.6%.

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38

Damanik, Hillman Akhyar. "SECURING DATA NETWORK FOR GROWING BUSINESS VPN ARCHITECTURES CELLULAR NETWORK CONNECTIVITY." Acta Informatica Malaysia 6, no. 1 (2022): 01–06. http://dx.doi.org/10.26480/aim.01.2022.01.06.

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Private networks allow organizations to leverage, customize and dedicate LTE network capabilities to their own service needs. These networks are controlled and managed locally and can be optimized for specific network services and applications. Often they are used because there may be gaps in coverage where there is no cellular connectivity. Enterprises and businesses running on light application systems, using small bandwidth and requiring fast deployment such as ATMs, vending machines, digital signage kiosks and small store branches We propose a method and concept and implementation, in the form of a Data Network Security Configuration For Business Growth VPN architecture Mobile Network Connectivity for various businesses. By implementing products and services by developing the configuration and model of VPN Tunneling Protocol rules, using the EOIP Protocol and SSTP Protocol methods and virtualization schemes using the VLAN Bridging method on Wide Area Network (WAN) network connectivity. This method takes advantage of the LTE features found on the RB751U-2hnd and then integrates Huawei Mobile Broadband LTE. We also present a general Service Level Agreement (SLA) and an open source tools-based SLA network system Zabbix. Then configure the VPN Tunneling Protocol and its features on the RB751U-2hnd using Paramiko Network Automation. The focus of the results of this research is that by utilizing the available tools we will build a VPN with system monitoring facilities with the results achieved are network performance and availability and show that the design we build can be used for private connections for growing businesses. the results obtained from testing for 1 month for the 3 providers used, for the average throughput value with VPN tunneling applied TSEL 14.2 Mbps, ISAT 13.9 Mbps and XL 13.5 Mbps. The SLA value obtained is based on acceptable criteria, TSEL 91.5%, ISAT 91.8% and XL 90.6%.

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39

de-la-Bandera, Isabel, David Palacios, Jessica Mendoza, and Raquel Barco. "Feature Extraction for Dimensionality Reduction in Cellular Networks Performance Analysis." Sensors 20, no. 23 (December 4, 2020): 6944. http://dx.doi.org/10.3390/s20236944.

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Next-generation mobile communications networks will have to cope with an extraordinary amount and variety of network performance indicators, causing an increase in the storage needs of the network databases and the degradation of the management functions due to the high-dimensionality of every network observation. In this paper, different techniques for feature extraction are described and proposed as a means for reducing this high dimensionality, to be integrated as an intermediate stage between the monitoring of the network performance indicators and their usage in mobile networks’ management functions. Results using a dataset gathered from a live cellular network show the benefits of this approach, in terms both of storage savings and subsequent management function improvements.

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40

Rajule, Nilakshee, Mithra Venkatesan, Radhika Menon, and Anju Kulkarni. "Network Traffic Prediction with Reduced Power Consumption towards Green Cellular Networks." International Journal of Computer Network and Information Security 15, no. 6 (December 8, 2023): 64–77. http://dx.doi.org/10.5815/ijcnis.2023.06.06.

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The increased number of cellular network subscribers is giving rise to the network densification in next generation networks further increasing the greenhouse gas emission and the operational cost of network. Such issues have ignited a keen interest in the deployment of energy-efficient communication technologies rather than modifying the infrastructure of cellular networks. In cellular network largest portion of the power is consumed at the Base stations (BSs). Hence application of energy saving techniques at the BS will help reduce the power consumption of the cellular network further enhancing the energy efficiency (EE) of the network. As a result, BS sleep/wake-up techniques may significantly enhance cellular networks' energy efficiency. In the proposed work traffic and interference aware BS sleeping technique is proposed with an aim of reducing the power consumption of network while offering the desired Quality of Service (QoS) to the users. To implement the BS sleep modes in an efficient manner the prediction of network traffic load is carried out for future time slots. The Long Short term Memory model is used for prediction of network traffic load. Simulation results show that the proposed system provides significant reduction in power consumption as compared with the existing techniques while assuring the QoS requirements. With the proposed system the power saving is enhanced by approximately 2% when compared with the existing techniques. His proposed system will help in establishing green communication networks with reduced energy and power consumption.

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41

Amuah, Ebenezer Ackah, Mingxiao Wu, and Xiaorong Zhu. "Cellular Network Fault Diagnosis Method Based on a Graph Convolutional Neural Network." Sensors 23, no. 16 (August 9, 2023): 7042. http://dx.doi.org/10.3390/s23167042.

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The efficient and accurate diagnosis of faults in cellular networks is crucial for ensuring smooth and uninterrupted communication services. In this paper, we propose an improved 4G/5G network fault diagnosis with a few effective labeled samples. Our solution is a heterogeneous wireless network fault diagnosis algorithm based on Graph Convolutional Neural Network (GCN). First, the common failure types of 4G/5G networks are analyzed, and then the graph structure is constructed with the data in the network parameter, given data sets as nodes and similarities as edges. GCN is used to extract features from the graph data, complete the classification task for nodes, and finally predict the fault types of cells. A large number of experiments are carried out based on the real data set, which is achieved by driving tests. The results show that, compared with a variety of traditional algorithms, the proposed method can effectively improve the performance of network fault diagnosis with a small number of labeled samples.

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42

Lorincz, Josip. "Energy-Efficient Wireless Cellular Communications through Network Resource Dynamic Adaptation." International Journal of Business Data Communications and Networking 9, no. 2 (April 2013): 11–26. http://dx.doi.org/10.4018/jbdcn.2013040102.

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Cellular networks represent one of the major energy consumers of communication networks and their contribution to the global carbon footprint and energy consumption continuously and rapidly increases. Improving energy efficiency of the cellular access networks become an important requirement and has recently gained considerable attention of the research community and operators. In this paper, improving cellular networks energy efficiency through dynamic adaptation of network resources is presented with foundations which justify practical realization of such approach. Paper gives insight into how the traffic pattern variations and transmitted power scaling influence on the instantaneous power consumption of the base stations. Also, impact of the base stations Tx power on two prominent energy efficiency metrics of the cellular access network is discussed. Results of a proposed optimization approach which is based on dynamic adaptation of the base stations on/off activity and the transmitted power in accordance with the spatial and temporal variations of traffic are presented. According to obtained results, dynamic adaptation of network resources can offer significant monthly energy savings on the level of complete cellular access network.

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Singh, Avinash, Surya Pratap Singh, Arvind Kumar Maurya, and Upendra Nath Tripathi. "Combined Strategy for Cellular Traffic Congestion Management." International Journal of Emerging Research in Management and Technology 6, no. 8 (June 25, 2018): 12. http://dx.doi.org/10.23956/ijermt.v6i8.113.

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In wireless media, congestion is usual phenomenon that arises due abruptly raised subscribers demand to establish connections congruently in particular cell at mean real time. Generally, the congestion problem occurs in cellular network traffic and almost persists in every generation. Every mobile network operators is facing this problem every day and struggling to resolve this issue but not being success yet. As more advance cellular devices are being in demand and its users are also being increasing which turns to demand larger bandwidth requirements from cellular subscriber. Presently for cellular network, both at infrastructures level and manual configuration, the network management strategy founds to be inefficient to resolve these issues effective way. While trying to resolve, at certain critical peak hours and occasions, situation gets worse. Since, every cellular operator are being facing strong criticism from both user and regulating authorities on this issue. An effort from subscriber’s end is to done only minor infrastructural changes to operate and maintain the network at low cost which found to ineffective and being failed to handle the communication demands of higher Quality of Service (QoS). One way of handling congestion would be through hardware amendment like sectorial design, cell partition, or some software enabled functions use to organize to automate the configuration, optimize and maintenance of cellular networks, But these changes involves heavy investment by subscriber which in turns to cost from user for its services. Concerning on this unresolved issue, our prime motive is to forward innovative effort work that could effectively and sustainable to manage the congestion for long duration. In this paper, we first review the various load sharing and balancing techniques those are used to resolve traffic congestion occurrences in mobile networks. And further presents an algorithm design that would helpful to develop a reliable, flexible, highly efficient load balancing and sharing system. The system based on proposed schema could manage the cell network resources with special emphasis techniques having suitable self optimization feature for future wireless cellular networks.

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Zhang, Dehai, Linan Liu, Cheng Xie, Bing Yang, and Qing Liu. "Citywide Cellular Traffic Prediction Based on a Hybrid Spatiotemporal Network." Algorithms 13, no. 1 (January 8, 2020): 20. http://dx.doi.org/10.3390/a13010020.

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With the arrival of 5G networks, cellular networks are moving in the direction of diversified, broadband, integrated, and intelligent networks. At the same time, the popularity of various smart terminals has led to an explosive growth in cellular traffic. Accurate network traffic prediction has become an important part of cellular network intelligence. In this context, this paper proposes a deep learning method for space-time modeling and prediction of cellular network communication traffic. First, we analyze the temporal and spatial characteristics of cellular network traffic from Telecom Italia. On this basis, we propose a hybrid spatiotemporal network (HSTNet), which is a deep learning method that uses convolutional neural networks to capture the spatiotemporal characteristics of communication traffic. This work adds deformable convolution to the convolution model to improve predictive performance. The time attribute is introduced as auxiliary information. An attention mechanism based on historical data for weight adjustment is proposed to improve the robustness of the module. We use the dataset of Telecom Italia to evaluate the performance of the proposed model. Experimental results show that compared with the existing statistics methods and machine learning algorithms, HSTNet significantly improved the prediction accuracy based on MAE and RMSE.

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Chandramohan, B. "Restructured Ant Colony Optimization Routing Protocol for Next Generation Network." International Journal of Computers Communications & Control 10, no. 4 (June 22, 2015): 492. http://dx.doi.org/10.15837/ijccc.2015.4.665.

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Wireless network is a major research domain in the past few decades. Wireless network evolves in many forms like cellular communication, ad hoc network, vehicular network, mesh network and sensor network. Next generation network is a recent cellular communication which provides heterogeneous connectivity on cellular communication. The routing in next generation wireless networks is an important research issue which requires many constraints than wired networks. Hence, Ant Colony Optimization (ACO) is applied in this paper for routing in heterogeneous next generation wireless network. The ACO is a swarm intelligence technique which applied for many engineering applications. ACO is an optimal technique for routing and travelling salesman problem. This paper proposed Restructured ACO which contains additional data structures for reducing packet loss and latency. Therefore, the proposed RACO provides higher throughput.

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Huang, Chien-Hung, Efendi Zaenudin, Jeffrey J. P. Tsai, Nilubon Kurubanjerdjit, Eskezeia Y. Dessie, and Ka-Lok Ng. "Dissecting molecular network structures using a network subgraph approach." PeerJ 8 (August 6, 2020): e9556. http://dx.doi.org/10.7717/peerj.9556.

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Biological processes are based on molecular networks, which exhibit biological functions through interactions of genetic elements or proteins. This study presents a graph-based method to characterize molecular networks by decomposing the networks into directed multigraphs: network subgraphs. Spectral graph theory, reciprocity and complexity measures were used to quantify the network subgraphs. Graph energy, reciprocity and cyclomatic complexity can optimally specify network subgraphs with some degree of degeneracy. Seventy-one molecular networks were analyzed from three network types: cancer networks, signal transduction networks, and cellular processes. Molecular networks are built from a finite number of subgraph patterns and subgraphs with large graph energies are not present, which implies a graph energy cutoff. In addition, certain subgraph patterns are absent from the three network types. Thus, the Shannon entropy of the subgraph frequency distribution is not maximal. Furthermore, frequently-observed subgraphs are irreducible graphs. These novel findings warrant further investigation and may lead to important applications. Finally, we observed that cancer-related cellular processes are enriched with subgraph-associated driver genes. Our study provides a systematic approach for dissecting biological networks and supports the conclusion that there are organizational principles underlying molecular networks.

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Boucherie, Richard J., and Nico M. van Dijk. "On a Queueing Network Model for Cellular Mobile Telecommunications Networks." Operations Research 48, no. 1 (February 2000): 38–49. http://dx.doi.org/10.1287/opre.48.1.38.12446.

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Huang, Kaibin, Marios Kountouris, and Victor O. K. Li. "Renewable Powered Cellular Networks: Energy Field Modeling and Network Coverage." IEEE Transactions on Wireless Communications 14, no. 8 (August 2015): 4234–47. http://dx.doi.org/10.1109/twc.2015.2418262.

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Xiong Ke, Fan Pingyi, Yi Su, and Lei Ming. "Network coding-aware cooperative relaying for downlink cellular relay networks." China Communications 10, no. 7 (July 2013): 44–56. http://dx.doi.org/10.1109/cc.2013.6570799.

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Zhou, Lixia, Xia Chen, Runsha Dong, and Shan Yang. "Hotspots Prediction Based on LSTM Neural Network for Cellular Networks." Journal of Physics: Conference Series 1624 (October 2020): 052016. http://dx.doi.org/10.1088/1742-6596/1624/5/052016.

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