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Journal articles on the topic 'Cellular network'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 June 2025

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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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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

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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6

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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7

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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8

K, Saravanan. "A Review on Green Cellular Network." International Journal of Trend in Scientific Research and Development 2, no. 5 (August 6, 2018): 342–49. https://doi.org/10.31142/ijtsrd15839.

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Energy efficiency in the cellular network is a growing concern for cellular operators and with the exponential increase in mobile internet traffic driven by a new generation of wireless devices, future cellular networks face a great challenge to meet this overwhelming demand of network capacity. At the same time, the demand for higher data rates and the ever increasing number of wireless users led to rapid increases in power consumption and operating cost of cellular networks. This emerging trend of achieving energy efficiency in cellular networks is motivating the standardization authorities and network operators to continuously explore future technologies in order to bring improvements in the entire network infrastructure. In this article, we present a brief review and analysis of methods to improve the power efficiency of cellular networks, explore some research issues and challenges and suggest some techniques to enable an energy efficient or "green" cellular network. K Saravanan "A Review on Green Cellular Network" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-5 , August 2018, URL: https://www.ijtsrd.com/papers/ijtsrd15839.pdf

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9

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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10

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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11

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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12

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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13

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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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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15

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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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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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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18

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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19

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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20

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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21

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>&nbsp;</p>

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22

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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23

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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24

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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25

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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26

Hiremath, Madhu N. "Software Defined Network (SDN) and OpenFlow Protocol in 5G Network." International Journal for Research in Applied Science and Engineering Technology 12, no. 11 (November 30, 2024): 2028–36. http://dx.doi.org/10.22214/ijraset.2024.65548.

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The world is moving at a high speed in the implementation and innovations of new systems and gadgets. 3G and 4G networks support currently wireless network communications. However, the networks are deemed to be slow and fail to receive signals or data transmission to various regions because of solving the problem. This paper will analyze the use of Software Defined Network (SDN) in a 5G (fifth generation) network that can be faster and reliable. Further, in Mobile IP, there exist triangulation problems between the sending and receiving nodes along with latency issues during handoff for the mobile nodes causing huge burden in the network. With Cloud Computing and ecosystem for Virtualization developed for the Core and Radio Networks SDN OpenFlow seems to be a seamless solution for determining signal flow between mobiles. There have been a lot of research going on for deploying SDN OpenFlow with the 5G Cellular Network. The current paper performs benchmarks as a feasibility need for implementing SDN OpenFlow for 5G Cellular Network. The Handoff mechanism impacts the scalability required for a cellular network and simulation results can be further used to be deployed the 5G Network.

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27

Cheng, Peng, Andrew R. Weily Weily, and Y. Jay Guo. "Towards 5th Generation Cellular Mobile Networks." Journal of Telecommunications and the Digital Economy 2, no. 2 (June 1, 2014): 14. https://doi.org/10.18080/jtde.v2n2.328.

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Cellular mobile networks have enabled ubiquitous communications and largely changed the way we live and work. At the same time, the network itself has been undergoing significant changes in the process of meeting our ever increasing demands on data rate and quality of service. In this article, we show the path of the evolution in both standards and techniques and provide our vision for the future of the cellular networks. We review the evolution of international standards for cellular mobile networks in the last two decades, describe how the network layout has been migrating from rigid cellular architecture to random and dense small cells, and provide an in-depth discussion on potential enabling techniques for the next generation (5G) cellular networks, particularly massive MIMO and multiband base-station antennas.

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28

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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29

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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30

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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31

Yunita Yunita, Muhammad Aziz Nur Mubarok, Arjuna Fikri Tri Firmansyah, and Didik Aribowo. "Penggunaan Software Cisco Packet Tracer Dalam Simulasi Jaringan Seluler Pada Pembelajaran Materi Terminal Telkomunikasi." Jupiter: Publikasi Ilmu Keteknikan Industri, Teknik Elektro dan Informatika 2, no. 3 (April 24, 2024): 26–33. http://dx.doi.org/10.61132/jupiter.v2i3.270.

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Cellular networks play an important role in modern life with the availability of flexible connectivity in various places, fast internet access and communication services and are a reference for innovation and economic growth. However, the complexity of cellular networks requires careful planning and management, this is to ensure a reliable and safe service. The method used in designing cellular networks is the simulation method, namely by using Cisco Packet Tracer software and literature studies from various references. The results of cellular network simulations using this software can show superiority in designing, configuring and testing cellular networks. Apart from that, this software also provides complete features to meet several needs, starting from the process of creating a network topology, configuring IP addresses, activating DHCP services and HTTP services, to testing connections using PDU samples between smartphone devices and servers. With the successful connection test, it can be concluded that the settings and services on the cellular network have been tested and verified using Cisco Packet Tracer. This has shown that the software used is very effective in simulating and testing cellular networks.

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32

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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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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Saad, Iqbal, Khan Alamgir, Wasim Khan Hassan, Khan Imran, and Muhammad Fazal. "Performance Enhancement of Heterogeneous Cellular Networks Using Vertical Handover." International Journal of Engineering Works (ISSN: 2409-2770) 06, no. 02 (February 15, 2019): 44–49. https://doi.org/10.5281/zenodo.2565880.

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  In seamless and fast mobility, smooth handover is one of the major challenges in small cell heterogeneous cellular networks. This paper focuses on the phenomenon of vertical handover for wireless heterogeneous networks. This class of networks can be grounded on the parameter values such as bandwidth availability, received signal strength, call request/served per unit time (mean), power dissipation, consumption of power, duration of mobile station presence, network security, cost of the network and velocity of the mobile station. The proposed system, in this paper, is characterized on the basis of four network parameters which are scrutinized as per a pre-defined criterion. Thereafter, vertical handover occurs at the most suitable among the available four networks. In order to achieve the objective, various parameters are applied for the vertical handover decision and implementation. Furthermore, the final decision is taken on the basis of individual network’s call blocking probability considering the aforesaid parameters.

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35

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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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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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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38

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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39

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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40

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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41

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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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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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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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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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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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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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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Shoewu, O.O. "Performance Evaluation Of Mobile Cellular Networks In Nigeria." J. of Advancement in Engineering and Technology 6, no. 3 (May 19, 2018): 05. https://doi.org/10.5281/zenodo.1250262.

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The aim of this paper is to evaluate the performance of mobile networks such as MTN, GLO, and ETISALAT in Nigeria and suggest ways the performance of digital cellular networks can improve to minimize some of its present short comings or limitations. This paper discusses the performance improvement of digital cellular networks. A non- CDMA cellular network is use in an overall wireless environment for the purpose of this paper. This paper also discusses the performance assessment of three mobile network operators. In addition, ways to minimize the limitations in other to improve the performance of digital mobile networks was also discussed. Finally, suggestions were made to improve QoS of mobile networks.

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Kahali, Sayan, Marcus E. Raichle, and Dmitriy A. Yablonskiy. "The Role of the Human Brain Neuron–Glia–Synapse Composition in Forming Resting-State Functional Connectivity Networks." Brain Sciences 11, no. 12 (November 27, 2021): 1565. http://dx.doi.org/10.3390/brainsci11121565.

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While significant progress has been achieved in studying resting-state functional networks in a healthy human brain and in a wide range of clinical conditions, many questions related to their relationship to the brain’s cellular constituents remain. Here, we use quantitative Gradient-Recalled Echo (qGRE) MRI for mapping the human brain cellular composition and BOLD (blood–oxygen level-dependent) MRI to explore how the brain cellular constituents relate to resting-state functional networks. Results show that the BOLD signal-defined synchrony of connections between cellular circuits in network-defined individual functional units is mainly associated with the regional neuronal density, while the between-functional units’ connectivity strength is also influenced by the glia and synaptic components of brain tissue cellular constituents. These mechanisms lead to a rather broad distribution of resting-state functional network properties. Visual networks with the highest neuronal density (but lowest density of glial cells and synapses) exhibit the strongest coherence of the BOLD signal as well as the strongest intra-network connectivity. The Default Mode Network (DMN) is positioned near the opposite part of the spectrum with relatively low coherence of the BOLD signal but with a remarkably balanced cellular contents, enabling DMN to have a prominent role in the overall organization of the brain and hierarchy of functional networks.

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Qiushi, Sun, He Yang, and Ovanes Petrosian. "Graph Attention Network Enhanced Power Allocation for Wireless Cellular System." Informatics and Automation 23, no. 1 (January 11, 2024): 259–83. http://dx.doi.org/10.15622/ia.23.1.9.

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The importance of an efficient network resource allocation strategy has grown significantly with the rapid advancement of cellular network technology and the widespread use of mobile devices. Efficient resource allocation is crucial for enhancing user services and optimizing network performance. The primary objective is to optimize the power distribution method to maximize the total aggregate rate for all customers within the network. In recent years, graph-based deep learning approaches have shown great promise in addressing the challenge of network resource allocation. Graph neural networks (GNNs) have particularly excelled in handling graph-structured data, benefiting from the inherent topological characteristics of mobile networks. However, many of these methodologies tend to focus predominantly on node characteristics during the learning phase, occasionally overlooking or oversimplifying the importance of edge attributes, which are equally vital as nodes in network modeling. To tackle this limitation, we introduce a novel framework known as the Heterogeneous Edge Feature Enhanced Graph Attention Network (HEGAT). This framework establishes a direct connection between the evolving network topology and the optimal power distribution strategy throughout the learning process. Our proposed HEGAT approach exhibits improved performance and demonstrates significant generalization capabilities, as evidenced by extensive simulation results.

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