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Journal articles on the topic 'Radio resource management'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Sallent, Oriol, Lorenza Giupponi, Jad Nasreddine, Ramon Agusti, and Jordi Perez-Romero. "Spectrum and radio resource management." IEEE Vehicular Technology Magazine 3, no. 4 (2008): 56–64. http://dx.doi.org/10.1109/mvt.2008.931527.

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2

Prasad, Athul, Anass Benjebbour, Omer Bulakci, Klaus I. Pedersen, Nuno K. Pratas, and Marco Mezzavilla. "Agile Radio Resource Management Techniques for 5G New Radio." IEEE Communications Magazine 55, no. 6 (2017): 62–63. http://dx.doi.org/10.1109/mcom.2017.7945854.

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3

Han, C., and S. Armour. "Energy efficient radio resource management strategies for green radio." IET Communications 5, no. 18 (2011): 2629–39. http://dx.doi.org/10.1049/iet-com.2011.0113.

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4

Zorzi, M. "Mobility support and radio resource management." IEEE Wireless Communications 11, no. 5 (2004): 2. http://dx.doi.org/10.1109/mwc.2004.1351674.

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5

Hills, A., and B. Friday. "Radio resource management in wireless LANs." IEEE Communications Magazine 42, no. 12 (2004): S9–14. http://dx.doi.org/10.1109/mcom.2004.1367553.

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6

Yanikomeroglu, Halim, Mohamed H. Ahmed, and Bassam Hashem. "Radio Resource Management for Wireless Internet." Wireless Communications and Mobile Computing 3, no. 7 (2003): 801–2. http://dx.doi.org/10.1002/wcm.182.

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7

Salazar, Jos ́e, Ismael Gómez, and Antoni Gelonch. "Adaptive Resource Management and Flexible Radios for WiMAX." Journal of Telecommunications and Information Technology, no. 4 (June 26, 2023): 101–7. http://dx.doi.org/10.26636/jtit.2009.4.996.

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The availability of dynamic resource management will be crucial for the deployment of future wireless systems characterized by high data rate services with rigid quality of service demands. Flexible radios appear as the technological answer required to achieve constraint goals under different channel conditions and transmission scenarios. This paper is focused on enhancing another step of flexibility within the resource management by including an efficient handling of computing resources. This concept towards flexible architectures represents a key word for a real successful implementation due

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8

Ma, Lu, Xiangming Wen, Luhan Wang, Zhaoming Lu, Raymond Knopp, and Irfan Ghauri. "A Biological Model for Resource Allocation and User Dynamics in Virtualized HetNet." Wireless Communications and Mobile Computing 2018 (September 27, 2018): 1–11. http://dx.doi.org/10.1155/2018/1745904.

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Virtualization technology is considered an effective measure to enhance resource utilization and interference management via radio resource abstraction in heterogeneous networks (HetNet). The critical challenge in wireless virtualization is virtual resource allocation on which substantial works have been done. However, most existing researches on virtual resource allocation focus on improving total utility. Different from the existing works, we investigate the dynamic-aware virtual radio resource allocation in virtualization based HetNet considering utility and fairness. A virtual radio resour

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9

Shyshatskyi, Andrii, Artur Melnyk, Oleksii Bondar, et al. "Justification of the methodological bases for the management of the radio resource of special purpose radio communication systems under conditions of prior uncertainty." Technology audit and production reserves 1, no. 2(57) (2021): 61–65. http://dx.doi.org/10.15587/2706-5448.2021.225017.

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The problem of substantiation of methodological bases of radio resource management of military radio communication systems in the conditions of a priori uncertainty is solved in the work. The object of research is the military radio communication system. One of the most problematic places in the management of military radio resources is the inability to carry out a hierarchical management of the parameters and modes of operation of both individual radios and the military radio system as a whole. This reduces the efficiency of the system itself and the efficiency of its application. The scienti

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10

Andrii, Shyshatskyi, Melnyk Artur, Bondar Oleksii, et al. "Justification of the methodological bases for the management of the radio resource of special purpose radio communication systems under conditions of prior uncertainty." Technology audit and production reserves 1, no. 2(57) (2021): 61–65. https://doi.org/10.15587/2706-5448.2021.225017.

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<em>The problem of substantiation of methodological bases of radio resource management of military radio communication systems in the conditions of a priori uncertainty is solved in the work. The object of research is the military radio communication system. One of the most problematic places in the management of military radio resources is the inability to carry out a hierarchical management of the parameters and modes of operation of both individual radios and the military radio system as a whole. This reduces the efficiency of the system itself and the efficiency of its application.</em> <e

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11

Devika SV, Et al. "Radio Resource Management Satellite Communication Network MCDM Method." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 10 (2023): 1826–32. http://dx.doi.org/10.17762/ijritcc.v11i10.8759.

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Worldwide deployment of heterogeneous wireless networks is growing as a result of consumer demand for connectivity at all times and in all places. These customers' interest in multimedia apps like video streaming and VoIP, which demand tight Quality of Service (QoS) support, is growing at the same time. With such limitations, provisioning network resources is a difficult undertaking. In fact, it might be challenging for a network operator to identify trustworthy criteria to choose the optimum network that ensures user happiness while maximising network utilisation, given the availability of nu

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12

Nonchev, Stanislav, and Mikko Valkama. "ADVANCED RADIO RESOURCE MANAGEMENT FOR MULTI ANTENNA PACKET RADIO SYSTEMS." International Journal of Wireless & Mobile Networks 2, no. 2 (2010): 1–14. http://dx.doi.org/10.5121/ijwmn.2010.2201.

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13

Gerasimenko, Mikhail, Dmitri Moltchanov, Roman Florea, et al. "Cooperative Radio Resource Management in Heterogeneous Cloud Radio Access Networks." IEEE Access 3 (2015): 397–406. http://dx.doi.org/10.1109/access.2015.2422266.

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14

Bourdena, Athina, Evangelos Pallis, Georgios Kormentzas, and George Mastorakis. "Efficient radio resource management algorithms in opportunistic cognitive radio networks." Transactions on Emerging Telecommunications Technologies 25, no. 8 (2013): 785–97. http://dx.doi.org/10.1002/ett.2687.

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15

Shaikh, Alam N., and S. K. Bodhe. "Comparison of Radio Resource Management in GSM." International Journal of Distributed and Parallel systems 2, no. 6 (2011): 153–65. http://dx.doi.org/10.5121/ijdps.2011.2614.

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16

Bi, Suzhi, Jiangbin Lyu, Zhi Ding, and Rui Zhang. "Engineering Radio Maps for Wireless Resource Management." IEEE Wireless Communications 26, no. 2 (2019): 133–41. http://dx.doi.org/10.1109/mwc.2019.1800146.

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17

Gomez, Ismael, Vuk Marojevic, and Antoni Gelonch. "Resource Management for Software-Defined Radio Clouds." IEEE Micro 32, no. 1 (2012): 44–53. http://dx.doi.org/10.1109/mm.2011.81.

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18

Zirwas, Wolfgang, Hui Li, Matthias Lott, Martin Weckerle, Egon Schulz, and Mattias Lampe. "Radio resource management in cellular multihop networks." European Transactions on Telecommunications 15, no. 4 (2004): 375–89. http://dx.doi.org/10.1002/ett.987.

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19

Brueck, Stefan, Enrico Jugl, Hans-Juergen Kettschau, Michael Link, Jens Mueckenheim, and Andrei Zaporozhets. "Radio resource management in HSDPA and HSUPA." Bell Labs Technical Journal 11, no. 4 (2007): 151–67. http://dx.doi.org/10.1002/bltj.20201.

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20

Sahinel, Doruk, Simon Rommel, and Idelfonso Tafur Monroy. "Resource Management in Converged Optical and Millimeter Wave Radio Networks: A Review." Applied Sciences 12, no. 1 (2021): 221. http://dx.doi.org/10.3390/app12010221.

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Three convergent processes are likely to shape the future of the internet beyond-5G: The convergence of optical and millimeter wave radio networks to boost mobile internet capacity, the convergence of machine learning solutions and communication technologies, and the convergence of virtualized and programmable network management mechanisms towards fully integrated autonomic network resource management. The integration of network virtualization technologies creates the incentive to customize and dynamically manage the resources of a network, making network functions, and storage capabilities at

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21

Jararweh, Yaser, Mahmoud Al-Ayyoub, Ahmad Doulat, Ahmad Al Abed Al Aziz, Haythem A. Bany Salameh, and Abdallah A. Khreishah. "Software Defined Cognitive Radio Network Framework." International Journal of Grid and High Performance Computing 7, no. 1 (2015): 15–31. http://dx.doi.org/10.4018/ijghpc.2015010102.

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Software defined networking (SDN) provides a novel network resource management framework that overcomes several challenges related to network resources management. On the other hand, Cognitive Radio (CR) technology is a promising paradigm for addressing the spectrum scarcity problem through efficient dynamic spectrum access (DSA). In this paper, the authors introduce a virtualization based SDN resource management framework for cognitive radio networks (CRNs). The framework uses the concept of multilayer hypervisors for efficient resources allocation. It also introduces a semi-decentralized con

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22

Bouleanu, Iulian, Dorin Alexandrescu, and Mircea Bora. "Radio Frequency Co-Site Management." International conference KNOWLEDGE-BASED ORGANIZATION 21, no. 3 (2015): 660–65. http://dx.doi.org/10.1515/kbo-2015-0112.

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Abstract The radio spectrum is a limited national resource, essential for some governmental applications and increasingly important for a series of non-governmental applications. The allocation of radio resources is done in a centralized manner, designating frequency managers of the defense system structures as local administrators of the resources allotted to the supported echelon. They have a limited number of frequencies they can assign to the emission sources in their area of responsibility. The article addresses the issue of radio spectrum management in the frequency allocation plans when

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23

Ding, Zhe, and Yubin Xu. "A Novel Joint Radio Resource Management with Radio Resource Reallocation in the Composite 3G Scenario." Information Technology Journal 10, no. 6 (2011): 1228–33. http://dx.doi.org/10.3923/itj.2011.1228.1233.

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24

Samidi, F. S., N. A. M. Radzi, W. S. H. M. W. Ahmad, F. Abdullah, M. Z. Jamaludin, and A. Ismail. "5G New Radio: Dynamic Time Division Duplex Radio Resource Management Approaches." IEEE Access 9 (2021): 113850–65. http://dx.doi.org/10.1109/access.2021.3104277.

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25

Sallent, Oriol, Jordi Perez-Romero, Ramon Ferrus, and Ramon Agusti. "On Radio Access Network Slicing from a Radio Resource Management Perspective." IEEE Wireless Communications 24, no. 5 (2017): 166–74. http://dx.doi.org/10.1109/mwc.2017.1600220wc.

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26

Huang, Chingyao, Hung-hui Juan, Meng-shiang Lin, and Chung-ju Chang. "Radio resource management of heterogeneous services in mobile WiMAX systems [Radio Resource Management and Protocol Engineering for IEEE 802.16]." IEEE Wireless Communications 14, no. 1 (2007): 20–26. http://dx.doi.org/10.1109/mwc.2007.314547.

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27

Shyshatskyi, Andrii, Volodymyr Ovchynnyk, Andrii Momotov, Nadiia Protas, and Andriy Solomakha. "Development of a mathematical model of radio resource management of special purpose radio communication systems based on an evolutionary approach." Technology audit and production reserves 1, no. 2(63) (2021): 31–36. http://dx.doi.org/10.15587/2706-5448.2022.251918.

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The object of research is a special-purpose radio communication system. A special purpose radio communication system is affected by many different destructive influences. The main ones are deliberate interference and cybernetic impact of various purposes. The above causes the search for new scientific approaches to identify and identify the destructive impact on special-purpose radio communications in order to increase the operational efficiency of special-purpose radio communications systems. In this work, the problems of developing a mathematical model for managing the radio resource of spec

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28

Wang, Hao, and Sha Sha. "Research on Wireless Resource Management." Applied Mechanics and Materials 727-728 (January 2015): 920–22. http://dx.doi.org/10.4028/www.scientific.net/amm.727-728.920.

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Wireless resource management is one of the key techniques in cognitive radio networks, which attracts great research attention, international cooperation project and other fundings. The dissertation contains valuable work on both theories and applications.A novel method using bipartite graph matching is proposed to realize the dynamic spectrum access. An idealized method and an operative method are presented respectively based on the famous Kuhn-Munkre algorithm. Experimental results show that proposed algorithms dramatically maximize the spectrum utilization and reduce allocation conflictions

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29

Ro, Cheul-Woo, and Kyung-Min Kim. "Radio Resource Management Modeling in IEEE 802.16e Networks." Journal of the Korea Contents Association 8, no. 1 (2008): 169–76. http://dx.doi.org/10.5392/jkca.2008.8.1.169.

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30

Bing, Cao, and Teresa A. Dahlberg. "Soft capacity modeling for WCDMA radio resource management." Journal of the Brazilian Computer Society 8, no. 3 (2003): 6–17. http://dx.doi.org/10.1590/s0104-65002003000100001.

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31

Lien, Shao-yu, Kwang-cheng Chen, Ying-chang Liang, and Yonghua Lin. "Cognitive radio resource management for future cellular networks." IEEE Wireless Communications 21, no. 1 (2014): 70–79. http://dx.doi.org/10.1109/mwc.2014.6757899.

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32

Kulkarni, Parag, Woon Hau Chin, and Tim Farnham. "Radio resource management considerations for LTE Femto cells." ACM SIGCOMM Computer Communication Review 40, no. 1 (2010): 26–30. http://dx.doi.org/10.1145/1672308.1672314.

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33

Awais, Muhammad, Ashfaq Ahmed, Syed Azhar Ali, Muhammad Naeem, Waleed Ejaz, and Alagan Anpalagan. "Resource Management in Multicloud IoT Radio Access Network." IEEE Internet of Things Journal 6, no. 2 (2019): 3014–23. http://dx.doi.org/10.1109/jiot.2018.2878511.

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34

Piamrat, Kandaraj, Adlen Ksentini, Jean-Marie Bonnin, and César Viho. "Radio resource management in emerging heterogeneous wireless networks." Computer Communications 34, no. 9 (2011): 1066–76. http://dx.doi.org/10.1016/j.comcom.2010.02.015.

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35

Malik, Hassan, Haris Pervaiz, Muhammad Mahtab Alam, Yannick Le Moullec, Alar Kuusik, and Muhammad Ali Imran. "Radio Resource Management Scheme in NB-IoT Systems." IEEE Access 6 (2018): 15051–64. http://dx.doi.org/10.1109/access.2018.2812299.

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36

Gozalvez, J., M. C. Lucas-Estañ, and J. Sanchez-Soriano. "Joint radio resource management for heterogeneous wireless systems." Wireless Networks 18, no. 4 (2011): 443–55. http://dx.doi.org/10.1007/s11276-011-0410-3.

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37

Sun, Wanlu, Erik G. Strom, Fredrik Brannstrom, Kin Cheong Sou, and Yutao Sui. "Radio Resource Management for D2D-Based V2V Communication." IEEE Transactions on Vehicular Technology 65, no. 8 (2016): 6636–50. http://dx.doi.org/10.1109/tvt.2015.2479248.

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38

Lincke, Susan J. "Vertical handover policies for common radio resource management." International Journal of Communication Systems 18, no. 6 (2005): 527–43. http://dx.doi.org/10.1002/dac.715.

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39

Taha, Abd-Elhamid M., Hossam S. Hassanein, and Hussein T. Mouftah. "Vertical handoffs as a radio resource management tool." Computer Communications 31, no. 5 (2008): 950–61. http://dx.doi.org/10.1016/j.comcom.2007.12.027.

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40

DR.A.K., KURESHI. "INTERFERENCE CONTROL AND RADIO RESOURCE MANAGEMENT IN FEMTOCELL." IJIERT - International Journal of Innovations in Engineering Research and Technology ICITER- 16 PUNE (June 20, 2016): 142–46. https://doi.org/10.5281/zenodo.1463595.

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<strong>Recently advancement in 4th generation (4G) heterogeneous networking has opened the door for the deployment of femtocells in a large scale. The deployment of femtocells in the existing macrocell networks and in 4G networks will significantly increase due to femtocells offer increase the coverage,the capacity and user friendly mode of operations in the both home and office environments. So,these low power home - based access points are going to change the landscape of the mobile technology,and the network business in the coming years.The objective of project is to study and propose the

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41

Andrii, Shyshatskyi, Ovchynnyk Volodymyr, Momotov Andrii, Protas Nadiia, and Solomakha Andriy. "Development of a mathematical model of radio resource management of special purpose radio communication systems based on an evolutionary approach." Technology audit and production reserves 1, no. 2(63) (2021): 31–36. https://doi.org/10.15587/2706-5448.2022.251918.

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<em>The object of research is a special-purpose radio communication system. A special purpose radio communication system is affected by many different destructive influences. The main ones are deliberate interference and cybernetic impact of various purposes. The above causes the search for new scientific approaches to identify and identify the destructive impact on special-purpose radio communications in order to increase the operational efficiency of special-purpose radio communications systems. In this work, the problems of developing a mathematical model for managing the radio resource of

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42

Rodoshi, Rehenuma Tasnim, Taewoon Kim, and Wooyeol Choi. "Resource Management in Cloud Radio Access Network: Conventional and New Approaches." Sensors 20, no. 9 (2020): 2708. http://dx.doi.org/10.3390/s20092708.

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Cloud radio access network (C-RAN) is a promising mobile wireless sensor network architecture to address the challenges of ever-increasing mobile data traffic and network costs. C-RAN is a practical solution to the strict energy-constrained wireless sensor nodes, often found in Internet of Things (IoT) applications. Although this architecture can provide energy efficiency and reduce cost, it is a challenging task in C-RAN to utilize the resources efficiently, considering the dynamic real-time environment. Several research works have proposed different methodologies for effective resource manag

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43

Lin, Gang Yong, Sheng Hui Dai, and Shan Long Ma. "An Improved Resource Management Method in Mobile Wireless Communications." Applied Mechanics and Materials 20-23 (January 2010): 872–77. http://dx.doi.org/10.4028/www.scientific.net/amm.20-23.872.

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The increasing use of wireless communications in mobile devices starts a new level of resource management. Users with mobile devices accessing wireless hot spots are a commonplace, and, thus, their management is becoming more important. Therefore, efficient utilization of radio resource becomes a key point in the design of broadband systems. From the view of the development of radio resource management, current technologies have quite good performance and high efficiency when the category of service is single. However, when multiple services take place simultaneously, the system has to emphasi

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44

Delaney, James, Steve Dowey, and Chi-Tsun Cheng. "Reinforcement-Learning-Based Robust Resource Management for Multi-Radio Systems." Sensors 23, no. 10 (2023): 4821. http://dx.doi.org/10.3390/s23104821.

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The advent of the Internet of Things (IoT) has triggered an increased demand for sensing devices with multiple integrated wireless transceivers. These platforms often support the advantageous use of multiple radio technologies to exploit their differing characteristics. Intelligent radio selection techniques allow these systems to become highly adaptive, ensuring more robust and reliable communications under dynamic channel conditions. In this paper, we focus on the wireless links between devices equipped by deployed operating personnel and intermediary access-point infrastructure. We use mult

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45

Shao, Yingxia, Hailin Jiang, and Hongli Zhao. "Stackelberg Game-Based Radio Resource Management Algorithm in an Urban Rail Transit Communication System." Urban Rail Transit 7, no. 2 (2021): 128–38. http://dx.doi.org/10.1007/s40864-021-00147-6.

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AbstractTrain-to-wayside (T2W) and train-to-train (T2T) communication modes may coexist in future train-centric communication-based train control (CBTC) systems. The feasibility of T2T communication in urban rail transit is analyzed first. Referring to the device-to-device (D2D) communication scenario in the general cellular network, this paper establishes a radio resource optimization model for the coexistence of train-to-train communication and train-to-wayside communication. With the aim of more efficient scheduling of radio time-frequency resources in the dedicated frequency band, we propo

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46

Peng, Wei, Dongyan Chen, Wenhui Sun, Chengdong Li, and Guiqing Zhang. "Interval type-2 fuzzy logic based radio resource management in multi-radio WSNs." Journal of Intelligent & Fuzzy Systems 35, no. 2 (2018): 2525–36. http://dx.doi.org/10.3233/jifs-182255.

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47

Shinkuma, Ryoichi, Minoru Okada, Katsutoshi Tsukamoto, and Shozo Komaki. "Radio Resource Management Algorithm Based on Required Data Quality." IEEJ Transactions on Electronics, Information and Systems 124, no. 2 (2004): 453–60. http://dx.doi.org/10.1541/ieejeiss.124.453.

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48

Wang, Ning, Ekram Hossain, and Vijay K. Bhargava. "Backhauling 5G small cells: A radio resource management perspective." IEEE Wireless Communications 22, no. 5 (2015): 41–49. http://dx.doi.org/10.1109/mwc.2015.7306536.

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49

Kuang, Linling, Xi Chen, Chunxiao Jiang, Haijun Zhang, and Sheng Wu. "Radio Resource Management in Future Terrestrial-Satellite Communication Networks." IEEE Wireless Communications 24, no. 5 (2017): 81–87. http://dx.doi.org/10.1109/mwc.2017.1700043.

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50

Arshad, Kamran, Richard Mackenzie, Ulrico Celentano, et al. "Resource management for QoS support in cognitive radio networks." IEEE Communications Magazine 52, no. 3 (2014): 114–20. http://dx.doi.org/10.1109/mcom.2014.6766095.

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