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Статті в журналах з теми "Mobile and Cognitive Radios"

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Автор: Grafiati

Опубліковано: 6 вересня 2023

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1

Haldorai, Anandakumar, Jeevanandham Sivaraj, Munivenkatappa Nagabushanam, and Michaelraj Kingston Roberts. "Cognitive Wireless Networks Based Spectrum Sensing Strategies: A Comparative Analysis." Applied Computational Intelligence and Soft Computing 2022 (October 30, 2022): 1–14. http://dx.doi.org/10.1155/2022/6988847.

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Анотація:

Because of numerous dormant application fields, wireless sensor networks (WSNs) have emerged as an important and novel area in radio and mobile computing research. These applications range from enclosed system configurations in the home and office to alfresco enlistment in an opponent’s landmass in a strategic flashpoint. Cognitive radio networks (CRNs) can be created by integrating radio link capabilities with network layer operations utilizing cognitive radios. The goal of CRN design is to optimize the general system operations to meet customer requirements at any location worldwide by much more efficiently addressing CRNs instead of simply connecting spectrum utilization. When compared to conventional radio networks, CRNs are more versatile and susceptible to wireless connections. Recent advancements in wireless communication have resulted in increasing spectrum scarcity. As a modern innovation, cognitive radio aims to tackle this challenge by proactively utilizing the spectrum. Because cognitive radio (CR) technology gives assailants additional possibilities than a normal wireless network, privacy in a CRN becomes a difficult challenge. We concentrate on examining the surveillance system at a societal level, in which both defense and monitoring are critical components in assuring the channel’s privacy. The current state of investigation into spectrum sensing and potential risks in cognitive radios is reviewed in this study.

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2

Duan, Meimei, Zhimin Zeng, Caili Guo, and Fangfang Liu. "User Selection for Cooperative Spectrum Sensing in Mobile Heterogeneous Cognitive Radios." Wireless Personal Communications 95, no. 3 (February 6, 2017): 3077–96. http://dx.doi.org/10.1007/s11277-017-3985-x.

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3

Silva, Francisco S. S., Andson M. Balieiro, Francisco F. de Mendoça, Kelvin L. Dias, and Paulo Guarda. "A Conformance Testing Methodology and System for Cognitive Radios." Wireless Communications and Mobile Computing 2021 (January 29, 2021): 1–15. http://dx.doi.org/10.1155/2021/8869104.

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Анотація:

The fifth generation (5G) of mobile networks has started its operation in some countries and is aimed at meeting demands beyond the current system capabilities such as the huge amount of connected devices from IoT applications (e.g., smart cities), explosive growth of high-speed mobile data traffic (e.g., ultrahigh definition video streaming), and ultrareliable and low latency communication (e.g., autonomous vehicle). To attend to these needs, the electromagnetic spectrum must be made available, but the static spectrum allocation policy has caused a spectrum shortage and impaired the employment/expansion of the wireless systems. To overcome this issue, the dynamic spectrum access (DSA) has been promoted in 5G/6G networks, which is enabled by the cognitive radio (CR) technology. Although diverse mechanisms have been developed to tackle the challenges that emerge in different CR layers/functionalities, a standardized testing methodology and system for CR is still immature. Existing standards or methodologies and systems for CR only focus on the definition of network technologies (e.g., IEEE 802.22 and IEEE 802.11af), performance evaluation of CR algorithms/mechanisms, or definition of the device cognition level via performance results or psychometric approaches, not covering systems/methodologies to verify if the device meets the CR capabilities and regulatory policies, neglecting the conformance testing. In this respect, this paper proposes a flexible methodology and system for CR conformance testing under two perspectives, functionalities and limits. We instantiate it by using the Universal Software Radio Peripheral (USRP) software-defined radio platform and present a proof-of-concept with a conformance metric. The results show the feasibility of our proposal.

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4

Wu, Chien-Min, Yen-Chun Kao, and Kai-Fu Chang. "A Multichannel MAC Protocol for IoT-enabled Cognitive Radio Ad Hoc Networks." Advances in Technology Innovation 5, no. 1 (January 1, 2020): 45–55. http://dx.doi.org/10.46604/aiti.2020.3946.

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Анотація:

Cognitive radios have the ability to dynamically sense and access the wireless spectrum, and this ability is a key factor in successfully building Internet-of-Things (IoT)-enabled mobile ad hoc networks. This paper proposes a contention-free token-based multichannel MAC protocol for IoT-enabled Cognitive Radio Ad Hoc Networks (CRAHNs). In this, secondary users of CRAHNs detect activity on the wireless spectrum and then access idle channels licensed by primary users. CRAHNs are divided into clusters, and the channel to use for transmission is determined dynamically from the probability of finding idle primary-user channels. The token-based MAC window size is adaptive, with adjustment according to actual traffic, which reduces both end-to-end MAC contention delay and energy consumption. High throughput and spatial reuse of channels can also be achieved using a dynamic control channel and dynamic schemes for contention windows. We performed extensive simulations to verify that the proposed method can achieve better performance in mobile CRAHNs than other MAC schemes can.

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5

Soares, Marilson Duarte, Diego Passos, and Pedro Vladimir Gonzalez Castellanos. "Cognitive Radio with Machine Learning to Increase Spectral Efficiency in Indoor Applications on the 2.5 GHz Band." Sensors 23, no. 10 (May 19, 2023): 4914. http://dx.doi.org/10.3390/s23104914.

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Анотація:

Due to the propagation characteristics in the 2.5 GHz band, the signal is significantly degraded by building entry loss (BEL), making coverage in indoor environments in some cases non-existent. Signal degradation inside buildings is a challenge for planning engineers, but it can be seen as a spectrum usage opportunity for a cognitive radio communication system. This work presents a methodology based on statistical modeling of data collected by a spectrum analyzer and the application of machine learning (ML) to leverage the use of those opportunities by autonomous and decentralized cognitive radios (CRs), independent of any mobile operator or external database. The proposed design targets using as few narrowband spectrum sensors as possible in order to reduce the cost of the CRs and sensing time, as well as improving energy efficiency. Those characteristics make our design especially interesting for internet of things (IoT) applications or low-cost sensor networks that may use idle mobile spectrum with high reliability and good recall.

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6

Yu, F., Minyi Huang, and Helen Tang. "Biologically inspired consensus-based spectrum sensing in mobile Ad Hoc networks with cognitive radios." IEEE Network 24, no. 3 (May 2010): 26–30. http://dx.doi.org/10.1109/mnet.2010.5464224.

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7

Guo, Songtao, Chuangyin Dang, and Xiaofeng Liao. "Distributed resource allocation with fairness for cognitive radios in wireless mobile ad hoc networks." Wireless Networks 17, no. 6 (June 19, 2011): 1493–512. http://dx.doi.org/10.1007/s11276-011-0360-9.

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8

Kim, Tae-Sung, Sok-Hyong Kim, Bo-Kyum Kim, and Young-Yong Kim. "Exploiting Multiple Channels for Low Latency and Semireliable Broadcasting in Cognitive Wireless Sensor Networks." International Journal of Distributed Sensor Networks 2015 (2015): 1–16. http://dx.doi.org/10.1155/2015/241208.

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Анотація:

In Wireless Sensor Networks (WSNs), disaster management is a crucial issue that focuses on disaster relief and recovery. Mobile sensor nodes support disaster relief and recovery by means of real-time bidirectional communication. For its high data rate requirement, IEEE 802.11 specification can be used for the radio interface of sensor nodes, and the nodes can be equipped with multiple 802.11 radios to utilize multiple channels and link data rates. Channel assignment algorithms can be applied in cognitive radio enabled networks which performs dynamic channel configuration for utilizing multiple channels. For efficient and semireliable broadcast in cognitive radio WSNs, we focus on reducing broadcast latency and achieving 100% delivery percentage. To realize these goals, in this study, we present our design for a novelChannel Assignment Algorithm for a Collision-Reduced Broadcast Tree(CA-CBT). Fundamentally, CA-CBT builds a broadcast tree and then uses several heuristic procedures to allocate collision-free channels to links on the tree. If CA-CBT fails to allocate collision-free channels due to a limited number of available channels, it allocates non-collision-free channels with the smallest number of interfering nodes. Through extensive simulations, we demonstrated that CA-CBT supports lower broadcast latency and higher delivery percentages compared with existing broadcast algorithms.

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9

Cacciapuoti, Angela Sara, Marcello Caleffi, Luigi Paura, and Md Arafatur Rahman. "Channel availability for mobile cognitive radio networks." Journal of Network and Computer Applications 47 (January 2015): 131–36. http://dx.doi.org/10.1016/j.jnca.2014.10.002.

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10

Lee, Jae-Joon, and Jaesung Lim. "Cognitive routing for multi-hop mobile cognitive radio ad hoc networks." Journal of Communications and Networks 16, no. 2 (April 2014): 155–61. http://dx.doi.org/10.1109/jcn.2014.000026.

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11

Lee, Chang-Woo. "Joint Clustering Method and Reinforcement Learning-Based Spectrum Management in Mobile Cognitive Radio Networks." Journal of Computational and Theoretical Nanoscience 18, no. 5 (May 1, 2021): 1535–39. http://dx.doi.org/10.1166/jctn.2021.9606.

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Анотація:

The spectrum management scheme is a crucial issue to utilize the limited resource and secure a higher quality of performance in mobile cognitive radio networks. We propose a spectrum management method to utilize the spectrum effectively and minimize the communication cost in mobile cognitive radio networks. We consider the following parts such as statistical information about spectrum access patterns and the number of spectrum hand-off. In addition, this paper proposes to make the parameters to decide the spectrum access of the secondary user during the primary user doesn’t use it. Those parameters are based on dynamic clustering and reinforcement learning. However, both the dynamic clustering method and reinforcement learning haven’t been studied much to minimize communication costs in mobile cognitive radio networks. Firstly, our study is to improve the opportunity of spectrum access through the analysis based on the spectrum patterns and the number of both the secondary user and the primary user in mobile cognitive radio networks. Secondly, we decrease the number of hand-offs because it checks the status of the available channel and the patterns of the spectrum usage in the clustering. Above all, the improvement of this paper is to cut down the cost of communication by utilizing the way of the optimal spectrum access in mobile cognitive radio networks.

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12

Chen, Hsing-Chung, Marsha Violetta, Chien-Erh Weng, and Tzu-Liang Kung. "Cognitive RBAC in mobile heterogeneous networks." Computer Science and Information Systems 10, no. 2 (2013): 779–806. http://dx.doi.org/10.2298/csis121110034c.

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Анотація:

In communication networks, a cognitive network (CN) is a new type of data network which is used to solve some of the problems that face current networks. Cognitive radio (CR) is part of a cognitive network and a smart wireless communication system. CR is conscious of its surrounding environment, and learns from the environment. It adapts its internal states by making corresponding real-time changes in certain operating parameters. In this paper, we propose a novel Cognitive RBAC (Role-Based Access Control) scheme which can be applied to Mobile Heterogeneous Networks (MHNs). The MHNs consist of mobile communication systems and Wi-Fi systems. The required new definitions for the RBAC model are proposed in this paper. They can improve the ability of conventional RBAC model to meet new challenges. In our scheme, we assume that a Cognitive Server (CS) provides and manages the permissions of services, and Network Providers support and manage a variety CRs and CNs, individually. For more efficiently managing CR and CN and meeting the large scale heterogeneous networks, we let mobile user can perceive network candidate actively to access services, in which the permissions are depending to the contract made by CS with each Network Provider. In this paper, the new generalized cognitive RBAC model and their definitions are proposed, and could be applied to new applications in a MHNs environment.

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13

Cadena-Muñoz, Ernesto, Luis Fernando Pedraza-Martínez, and Enrique Rodríguez-Colina. "Primary user emulation characterization in mobile cognitive radio networks." Visión electrónica 14, no. 1 (January 31, 2020): 26–34. http://dx.doi.org/10.14483/22484728.16351.

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Анотація:

This paper presents the results of the characterization of the attack known as the "primary user emulation" in mobile cognitive radio networks performing the implementation and testing. The tools and their configuration to carry out the attack are presented and their effects on the network are analyzed. The results show how to generate the attack with a software-defined radio equipment (SDR) using GNU-Radio and OpenBTS. The effects of the possible configurations of the attack on the network are shown, the malicious type generates constant interference on the primary or cognitive network, the selfish type allows to imitate a licensed or primary user generating interference to the primary network and inability to access the Cognitive Network while active. If the emulator's power level is fixed, the services it provides are stable. If the power is variable the services suffer intermittency. Primary user emulation is the attack that most affects the cognitive radio network so its effects are analyzed in order to propose ways of detecting or applying countermeasures.

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14

R, Valanarasu, and Christy A. "COMPREHENSIVE SURVEY OF WIRELESS COGNITIVE AND 5G NETWORKS." Journal of Ubiquitous Computing and Communication Technologies 01, no. 01 (September 13, 2019): 23–32. http://dx.doi.org/10.36548/jucct.2019.1.003.

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Анотація:

The tremendous progress in the wireless mobile communication devices and the continuous growth in the data rates and the usage with the entailment of enhanced performance in the network in the terms of the throughput, delay, energy utilization, data capacity, bandwidth utilization and the cost have led to the emergence of the 5G networks. The flexible transmission empowered by the cognitive radio networks make them prominent in the transmission of the applications that are significant and sensitive to latency. The paper surveys the details of the 5G network, the cognitive radio networks and the impacts in the network by having the cognitive radio in the 5G mobile wireless networks.

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15

Kilaru, Seetaiah, and Adithya Gali. "Energy Efficiency in Cognitive Radio Network: Green Technology Towards Next Generation Networks." International Journal of Informatics and Communication Technology (IJ-ICT) 5, no. 1 (April 1, 2016): 45. http://dx.doi.org/10.11591/ijict.v5i1.pp45-50.

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Energy efficiency of mobile network is always a challenging task. From the past decade, it is observable that the users who are using multimedia services are increasing in rapid way. These multimedia applications require higher data rates. High data rates will consume more energy of mobile network, which results poor energy efficiency. To meet higher data rates and to achieve energy efficiency, Cognitive Mobile Network with small cell model was explained in this paper. Dynamics of the power grid also have significant impact on mobile networks, hence smart grid implementation was proposed instead of traditional power grid. Most of the existed studies on cognitive mobile network focussed on spectrum sensing only. This paper focussed on cognitive radio network implementation by considering spectrum sensing and smart grid environment. An iterative algorithm was proposed to attain equillibrium condition to the problem. Interference management and energy efficient power allocation were achieved with the introduction of smart grid. Simulation results proved that optimum power allocation and energy efficiency are possible with the introduction of smart grid in cognitive network.

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16

PABLO, PALACIOS, and CASTRO ALBERTO. "COGNITIVE RADIO SIMULATOR FOR MOBILE NETWORKS: DESIGN AND IMPLEMENTATION." i-manager's Journal on Communication Engineering and Systems 7, no. 2 (2018): 1. http://dx.doi.org/10.26634/jcs.7.2.14441.

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17

Jana, Shraboni, Kai Zeng, Wei Cheng, and Prasant Mohapatra. "Trusted Collaborative Spectrum Sensing for Mobile Cognitive Radio Networks." IEEE Transactions on Information Forensics and Security 8, no. 9 (September 2013): 1497–507. http://dx.doi.org/10.1109/tifs.2013.2273305.

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18

Cacciapuoti, Angela Sara, Marcello Caleffi, and Luigi Paura. "Reactive routing for mobile cognitive radio ad hoc networks." Ad Hoc Networks 10, no. 5 (July 2012): 803–15. http://dx.doi.org/10.1016/j.adhoc.2011.04.004.

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19

Ali, Dr Esmail. "EFFECT OF MAGNETIC FIELD FROM MOBILE PHONE ON CENTRAL NERVOUS SYSTEM." JOURNAL OF ADVANCES IN PHYSICS 8, no. 3 (May 18, 2015): 2267–77. http://dx.doi.org/10.24297/jap.v8i3.1491.

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Анотація:

Devices such as mobile phones, wireless internet modems, and radios and televisions, which occupy an important place in social life, produce electromagnetic fields (EMFs). Widespread use of these devices in daily life increases the intensity of exposure to EMFs on a day to day basis. Investigation of the effects on health of devices such as mobile phones used in close proximity to the body is attracting considerable interest from scientists. Mobile phones manufactured using the latest technology operate in a high frequency range (300â€“3000 MHz). This further heightens concerns regarding the effect of mobile phones on human health. Most Global System for Mobile Communications (GSM) operators in Europe, Asia, and Africa use a frequency of 900 MHz. With the rapid development of electronic information and communication techniques, exposure to electromagnetic fields (EMFs) has increased dramatically. Some studies have focused on the biological effects of electromagnetic radiation. Microwave radiation has been reported as producing adverse effects in the central nervous system (CNS), including headache, sleep disorders, anxiety, cognitive dysfunction and neurogenesis impairment in both humans and animals. However, the direct effects of microwave radiation on neurodevelopment and the underlying mechanisms for any such effects remain unknown. As per todayâ€™s global scenario use of mobile phone is increasing day by day for communication. Due to its constant use, the electromagnetic radiation (EMR) emitted from the cell phone, base station and other household appliances cause adverse effects on human health. There is an increase concern about the interaction of EMR generated from mobile phones, with the human organs specially with brain because of its close and long proximity to human brain during the mobile usage. Concerns have shown whether these exposures could have effect on brain and central nervous system (CNS).

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20

Sharanya, C., M. Meena, M. Monisha, Dr V. Rajendran, and . "Development of efficient VoIP application using cognitive radio networks." International Journal of Engineering & Technology 7, no. 3.3 (June 8, 2018): 419. http://dx.doi.org/10.14419/ijet.v7i2.33.14200.

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Анотація:

There is an increased usage of wireless communication for personal use, commercial use etc. Nowadays Cognitive Radio has become the prime topic of interest in wireless network. It is an intelligent radio technology, which easily adapts to the network and automatically detects the vacant channels which are available in a wireless spectrum. It then changes the transmission parameters enabling concurrent communica-tions thereby improving the radio operating behavior. VoIP mobile applications are used worldwide for making audio and video calls using internet. But they face many disadvantages such as reflection of voice/echo, breaking of voice, delay in voice etc and have reduced quality with low Wi-Fi or 3g signals. Therefore we are designing an efficient and effective system to improve throughput and bit error rate of VoIP mobile applications by implementing them using cognitive radio networks.

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21

Omar, Shereen, Osama El Ghandour, and Ahmed M. Abd El-Haleem. "Multipath Activity Based Routing Protocol for Mobile ‎Cognitive Radio Ad Hoc Networks." Wireless Communications and Mobile Computing 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/5380525.

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Анотація:

Cognitive radio networks improve spectrum utilization by ‎sharing licensed spectrum with cognitive radio devices. In ‎cognitive radio ad hoc networks the routing protocol is one ‎of the most challenging tasks due to the changes in ‎frequency spectrum and the interrupted connectivity ‎caused by the primary user activity. In this paper, a multi‎path activity based routing protocol for cognitive radio ‎network (MACNRP) is proposed. The protocol utilizes ‎channel availability and creates multiple node-disjoint ‎routes between the source and destination nodes. The ‎proposed protocol is compared with D2CARP and FTCRP ‎protocols. The performance evaluation is conducted ‎through mathematical analysis and using OPNET ‎simulation. The performance of the proposed protocol ‎achieves an increase in network throughput; besides it ‎decreases the probability of route failure due to node ‎mobility and primary user activity. We have found that the ‎MACNRP scheme results in 50% to 75% reduction in ‎blocking probability and 33% to 78% improvement in ‎network throughput, with a reasonable additional routing ‎overhead and average packet delay. Due to the successful ‎reduction of collision between primary users and ‎cognitive users, the MACNRP scheme results in decreasing ‎the path failure rate by 50% to 87%.‎

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22

Zhai, Linbo, Hua Wang, and Chengcheng Liu. "Distributed Schemes for Crowdsourcing-Based Sensing Task Assignment in Cognitive Radio Networks." Wireless Communications and Mobile Computing 2017 (2017): 1–8. http://dx.doi.org/10.1155/2017/5017653.

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Анотація:

Spectrum sensing is an important issue in cognitive radio networks. The unlicensed users can access the licensed wireless spectrum only when the licensed wireless spectrum is sensed to be idle. Since mobile terminals such as smartphones and tablets are popular among people, spectrum sensing can be assigned to these mobile intelligent terminals, which is called crowdsourcing method. Based on the crowdsourcing method, this paper studies the distributed scheme to assign spectrum sensing task to mobile terminals such as smartphones and tablets. Considering the fact that mobile terminals’ positions may influence the sensing results, a precise sensing effect function is designed for the crowdsourcing-based sensing task assignment. We aim to maximize the sensing effect function and cast this optimization problem to address crowdsensing task assignment in cognitive radio networks. This problem is difficult to be solved because the complexity of this problem increases exponentially with the growth in mobile terminals. To assign crowdsensing task, we propose four distributed algorithms with different transition probabilities and use a Markov chain to analyze the approximation gap of our proposed schemes. Simulation results evaluate the average performance of our proposed algorithms and validate the algorithm’s convergence.

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23

Csurgai-Horváth, László, István Rieger, and József Kertész. "A Survey of the DVB-T Spectrum: Opportunities for Cognitive Mobile Users." Mobile Information Systems 2016 (2016): 1–11. http://dx.doi.org/10.1155/2016/3234618.

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Анотація:

Cognitive radio (CR) systems are designed to utilize the available radio spectrum in an efficient and intelligent manner. Terrestrial Digital Video Broadcasting (DVB-T) frequency bands are one of the future candidates for cognitive radio applications especially because after digital television transition the TV white spaces (TVWS) became available for radio communication. This paper deals with the survey of the DVB-T spectrum; wideband measurements were performed on mobile platform in order to study the variation of the radio signal power in city area aboard a moving vehicle. The measurement environment was a densely built-in region where the proper DVB-T receiving was guaranteed by three TV transmitters, utilizing three central channel frequencies using 610, 746, and 770 MHz. In our paper the methods, the applied antenna, and measurement devices will be presented together with simulated and measured fading statistics. The final result is an estimation of the cognitive DVB-T spectrum utilization opportunity; furthermore a scenario is also proposed for secondary channel usage.

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24

De, Parnika, and Shailendra Singh. "Journey of Mobile Generation and Cognitive Radio Technology in 5G." International Journal of Mobile Network Communications & Telematics 6, no. 4/5/6 (December 30, 2016): 01–19. http://dx.doi.org/10.5121/ijmnct.2016.6601.

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25

Wang, Yao. "SVM-based Spectrum Handoff Scheme for Mobile Cognitive Radio Networks." Journal of Information and Computational Science 12, no. 4 (March 1, 2015): 1301–9. http://dx.doi.org/10.12733/jics20105392.

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26

Huang, Jianhui, Shengling Wang, Xiuzhen Cheng, Min Liu, Zhongcheng Li, and Biao Chen. "Mobility-Assisted Routing in Intermittently Connected Mobile Cognitive Radio Networks." IEEE Transactions on Parallel and Distributed Systems 25, no. 11 (November 2014): 2956–68. http://dx.doi.org/10.1109/tpds.2013.291.

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27

Tsukamoto, Kazuya, Shun Koba, Masato Tsuru, and Yuji Oie. "Cognitive Radio-Aware Transport Protocol for Mobile Ad Hoc Networks." IEEE Transactions on Mobile Computing 14, no. 2 (February 1, 2015): 288–301. http://dx.doi.org/10.1109/tmc.2014.2320267.

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28

Liu, Miao, Tiecheng Song, Jing Hu, Hikmet Sari, and Guan Gui. "Anti-Shadowing Resource Allocation for General Mobile Cognitive Radio Networks." IEEE Access 6 (2018): 5618–32. http://dx.doi.org/10.1109/access.2018.2792679.

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29

He, Xiaofan, Richeng Jin, and Huaiyu Dai. "Camouflaging Mobile Primary Users in Database-Driven Cognitive Radio Networks." IEEE Wireless Communications Letters 8, no. 1 (February 2019): 21–24. http://dx.doi.org/10.1109/lwc.2018.2846621.

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30

El Misilmani, H. M., M. Y. Abou-Shahine, Y. Nasser, and K. Y. Kabalan. "Recent Advances on Radio-Frequency Design in Cognitive Radio." International Journal of Antennas and Propagation 2016 (2016): 1–16. http://dx.doi.org/10.1155/2016/9878475.

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Анотація:

With the growth of mobile data applications, the spectrum allocation is becoming very scarce. To ease congestion and boost speeds, cognitive radio (CR) is currently seen as a major solution and expected to be the key player in the new wireless technologies. In this paper, we will start by introducing the cognitive radio systems, followed by exploring the challenges in designing RF engine, along with an investigation of its antennas, amplifiers, oscillators, and the components that are expected to operate over a wide range of frequencies.

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31

Palanisamy, Ramesh, Senthil Jayapal, Anand M, and Jeelani Basha. "ENHANCED HYBRID COGNITIVE RADIO AD HOC NETWORKS (CRAHNs) FOR MOBILE NETWORK." International Journal of Computer Science and Mobile Computing 11, no. 11 (November 30, 2022): 237–46. http://dx.doi.org/10.47760/ijcsmc.2022.v11i11.020.

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Анотація:

In sensor networks, a very crucial aspect of the maintenance of the communications of secure data is the data, which is the most difficult of all tasks. The sensor network consists of hierarchical elements like the Base Station (BS), the Cluster Head (CH) and the Sensor Nodes (SNs), and this will have three different keys, which are the public and private, the cluster and the master keys. Through the opportunistic use of the currently available wireless spectrum, cognitive radio (CR) technology is intended to address the issues in wireless networks brought on by the limited amount of range accessible and the inefficient use of spectrum. With the inherent capabilities of cognitive radio, CR networks will offer the most advanced spectrum-aware communication paradigm in wireless communications. However, the significant spectral fluctuation and various quality-of-service (QoS) requirements faced by CR networks provide unique difficulties. The distributed multihop design, the dynamic network topology, and the time- and location-varying spectrum availability are essential differentiating elements in cognitive radio ad hoc networks (CRAHNs).

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32

Zhai, Linbo, and Hua Wang. "Crowdsensing Task Assignment Based on Particle Swarm Optimization in Cognitive Radio Networks." Wireless Communications and Mobile Computing 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/4687974.

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Cognitive radio technology allows unlicensed users to utilize licensed wireless spectrum if the wireless spectrum is unused by licensed users. Therefore, spectrum sensing should be carried out before unlicensed users access the wireless spectrum. Since mobile terminals such as smartphones are more and more intelligent, they can sense the wireless spectrum. The method that spectrum sensing task is assigned to mobile intelligent terminals is called crowdsourcing. For a large-scale region, we propose the crowdsourcing paradigm to assign mobile users the spectrum sensing task. The sensing task assignment is influenced by some factors including remaining energy, locations, and costs of mobile terminals. Considering these constraints, we design a precise sensing effect function with a local constraint and aim to maximize this sensing effect to address crowdsensing task assignment. The problem of crowdsensing task assignment is difficult to solve since we prove that it is NP-hard. We design an optimal algorithm based on particle swarm optimization to solve this problem. Simulation results show our algorithm achieves higher performance than the other algorithms.

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33

Patkar, Ankit. "5G Wireless Technology." International Journal for Research in Applied Science and Engineering Technology 10, no. 3 (March 31, 2022): 1519–24. http://dx.doi.org/10.22214/ijraset.2022.40930.

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Abstract: After the up rise of 4G wireless mobile technology takes place; researchers, mobile operator industries representative, academic institutions have started to look into the advancement (technological) towards 5G communication networks due to some main demands that are meliorated data rates, better capacity, minimized latency and better QoS (Quality of Service). To established the 5G mobile communication technological foundation, various research works or projects entailing main mobile infrastructure manufacturers, academia and international mobile network operators have been introduced recently. Nevertheless, 5G mobile services to be made available for use, their architecture, and their performance have not been evidently explicated. In this paper, we represent thorough overview of 5G the next generation mobile technology. We mainly throws light on 5G network architecture, 5G radio spectrum, ultra-dense radio access networks (UDRAN), traffic offloading of mobile, cognitive radio (CR), software defined radio (SDR), software defined networking (SDN), mixed infrastructure, and 5G network impact on the society. Keywords: 5G wireless technology, evolution from 4G to 5G

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34

Martinez, Julian, Cesar Hernandez, and Luis Pedraza. "Spectrum sharing in cognitive radio networks." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 6 (December 1, 2020): 6472. http://dx.doi.org/10.11591/ijece.v10i6.pp6472-6483.

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Cognitive radio networks are the next step to tackle scarcity in wireless networks given the increasing demand of radioelectric spectrum where the proposed solution is to share said resource to improve this situation. In the present article, a review of the current state of spectrum sharing in cognitive radio networks. To achieve this purpose, the articles published over the last 4 years on the matter were reviewed including topics such as mobile networks and TV. Some studies and simulations proposed to share the spectrum is shown. The current state of the studies reveals that there has been significant progress in this research area yet it is necessary to continue similar studies and set in motion different schemes.

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35

Muñoz, Ernesto Cadena, Luis Fernando Pedraza, and Cesar Augusto Hernández. "Machine Learning Techniques Based on Primary User Emulation Detection in Mobile Cognitive Radio Networks." Sensors 22, no. 13 (June 21, 2022): 4659. http://dx.doi.org/10.3390/s22134659.

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Mobile cognitive radio networks (MCRNs) have arisen as an alternative mobile communication because of the spectrum scarcity in actual mobile technologies such as 4G and 5G networks. MCRN uses the spectral holes of a primary user (PU) to transmit its signals. It is essential to detect the use of a radio spectrum frequency, which is where the spectrum sensing is used to detect the PU presence and avoid interferences. In this part of cognitive radio, a third user can affect the network by making an attack called primary user emulation (PUE), which can mimic the PU signal and obtain access to the frequency. In this paper, we applied machine learning techniques to the classification process. A support vector machine (SVM), random forest, and K-nearest neighbors (KNN) were used to detect the PUE in simulation and emulation experiments implemented on a software-defined radio (SDR) testbed, showing that the SVM technique detected the PUE and increased the probability of detection by 8% above the energy detector in low values of signal-to-noise ratio (SNR), being 5% above the KNN and random forest techniques in the experiments.

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36

Singh, Neeta, and Jyoti Singh. "QUEUEING ANALYSIS OF MOBILE ADHOC NETWORKS EQUIPPED WITH COGNITIVE RADIO TECHNOLOGY." ICTACT Journal on Communication Technology 9, no. 4 (December 1, 2018): 1875–79. http://dx.doi.org/10.21917/ijct.2018.0273.

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37

Chatterjee, Swagata Roy, Mohuya Chakraborty, and Jayanta Chakraborty. "Cognitive Radio Sensor Node Empowered Mobile Phone for Explosive Trace Detection." International Journal of Communications, Network and System Sciences 04, no. 01 (2011): 33–41. http://dx.doi.org/10.4236/ijcns.2011.41004.

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38

Liu, Boyang, Jin Wang, Shuai Ma, Fuhui Zhou, Yujiao Ma, and Guangyue Lu. "Energy-Efficient Cooperation in Mobile Edge Computing-Enabled Cognitive Radio Networks." IEEE Access 7 (2019): 45382–94. http://dx.doi.org/10.1109/access.2019.2909319.

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39

Wang, Yao, Zhongzhao Zhang, Lin Ma, and Jiamei Chen. "SVM-Based Spectrum Mobility Prediction Scheme in Mobile Cognitive Radio Networks." Scientific World Journal 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/395212.

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Spectrum mobility as an essential issue has not been fully investigated in mobile cognitive radio networks (CRNs). In this paper, a novel support vector machine based spectrum mobility prediction (SVM-SMP) scheme is presented considering time-varying and space-varying characteristics simultaneously in mobile CRNs. The mobility of cognitive users (CUs) and the working activities of primary users (PUs) are analyzed in theory. And a joint feature vector extraction (JFVE) method is proposed based on the theoretical analysis. Then spectrum mobility prediction is executed through the classification of SVM with a fast convergence speed. Numerical results validate that SVM-SMP gains better short-time prediction accuracy rate and miss prediction rate performance than the two algorithms just depending on the location and speed information. Additionally, a rational parameter design can remedy the prediction performance degradation caused by high speed SUs with strong randomness movements.

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40

Du, Jun, Daoxing Guo, Bangning Zhang, and Ligang Shang. "A Secure Cooperative Spectrum Sensing Scheme in Mobile Cognitive Radio Networks." International Journal of Distributed Sensor Networks 10, no. 6 (January 2014): 620196. http://dx.doi.org/10.1155/2014/620196.

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41

Wang, Xinyu, Min Jia, Qing Guo, Xuemai Gu, and Guangyu Zhang. "Reputation-based cooperative spectrum sensing algorithm for mobile cognitive radio networks." China Communications 14, no. 1 (January 2017): 124–34. http://dx.doi.org/10.1109/cc.2017.7839763.

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42

Wu, Jun, Cong Wang, Yue Yu, Tiecheng Song, and Jing Hu. "Performance optimisation of cooperative spectrum sensing in mobile cognitive radio networks." IET Communications 14, no. 6 (April 1, 2020): 1028–36. http://dx.doi.org/10.1049/iet-com.2019.1083.

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43

Chowdhury, K. R., and M. D. Felice. "Search: A routing protocol for mobile cognitive radio ad-hoc networks." Computer Communications 32, no. 18 (December 2009): 1983–97. http://dx.doi.org/10.1016/j.comcom.2009.06.011.

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44

Marwanto, Arief, Sharifah Kamilah Syed Yusof, and Muhammad Haikal Satria. "Adaptive quantization for spectrum exchange information in mobile cognitive radio networks." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 4 (August 1, 2020): 3605. http://dx.doi.org/10.11591/ijece.v10i4.pp3605-3614.

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To reduce the detection failure of the exchanging signal power onto the OFDM subcarrier signal at uniform quantization, dynamic subcarrier mapping is applied. Moreover, to addressing low SNR’s wall-less than pre-determine threshold, non-uniform quantization or adaptive quantization for the signal quantization size parameter is proposed. μ-law is adopted for adaptive quantization subcarrier mapping which is deployed in mobility environment, such as Doppler Effect and Rayleigh Fading propagation. In this works, sensing node received signal power then sampled into a different polarity positive and negative in μ-law quantization and divided into several segmentation levels. Each segmentation levels are divided into several sub-segment has representing one tone signal subcarrier number OFDM which has the number of quantization level and the width power. The results show that by using both methods, a significant difference is obtained around 8 dB compared to those not using the adaptive method.

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45

Kianoush, Sanaz, Anna Vizziello, and Paolo Gamba. "Energy-Efficient and Mobile-Aided Cooperative Localization in Cognitive Radio Networks." IEEE Transactions on Vehicular Technology 65, no. 5 (May 2016): 3450–61. http://dx.doi.org/10.1109/tvt.2015.2441733.

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46

Vikas, Vikas, and V. R. Singh. "Transmission Power Control For Fixed and Mobile Cognitive Radio Adhoc Networks." IOSR Journal of Electronics and Communication Engineering 12, no. 04 (July 2017): 14–20. http://dx.doi.org/10.9790/2834-1204011420.

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47

Kumar Reddy, P. Lokesh, B. Rama Bhupal Reddy, and S. Rama Krishna. "Cognitive Radio Sensor Node Empowered Mobile Phone for Explosive Trace Detection." International Journal of Computer Network and Information Security 4, no. 9 (August 14, 2012): 29–37. http://dx.doi.org/10.5815/ijcnis.2012.09.04.

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48

Talay, A. C., and D. T. Altilar. "United nodes: cluster-based routing protocol for mobile cognitive radio networks." IET Communications 5, no. 15 (October 14, 2011): 2097–105. http://dx.doi.org/10.1049/iet-com.2010.0285.

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49

Sabat, Santoshkumar, Prabhat Kumar Sharma, and Abhay Gandhi. "Full-duplex mobile cognitive radio network under Nakagami-m fading environment." AEU - International Journal of Electronics and Communications 109 (September 2019): 136–45. http://dx.doi.org/10.1016/j.aeue.2019.06.031.

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50

Xiao, Yalong, Jianxin Wang, Shigeng Zhang, and Jiannong Cao. "An energy-preserving spectrum access strategy in mobile cognitive radio networks." Transactions on Emerging Telecommunications Technologies 25, no. 8 (December 22, 2013): 865–74. http://dx.doi.org/10.1002/ett.2775.

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