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Journal articles on the topic 'Radio resource management'

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Author: Grafiati

Published: 4 June 2021

Last updated: 25 May 2024

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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 (December 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 (December 16, 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 (October 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 (December 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 to the relationship between the radio applications, which face the scarcity of resources within a heterogeneous environment, and the processing power needed to execute them.

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8

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 (November 2, 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 numerous access technologies (WiFi, WiMAX, or cellular networks). To solve this problem, each eNB just needs to learn the traffi c conditions or patterns of its owncell in our proposal. Wireless communication systems depend heavily on radio resource management (RRM). To ensure the efficient and successful operation of wireless networks, it involves the allocation and control of radio frequency spectrum, power, and other resources. RRM is significant because it can use scarce radio resources as efficiently as possible, enhancing capacity, lowering interference, and improving service quality. Successful deployment and operation of wireless communication systems like cellular networks, Wi-Fi, and Bluetooth depend on effective RRM approaches. The need for wireless communication is growing, and new technologies and standards are constantly being developed. The methodology of radio resource management (RRM) involves a variety of techniques and algorithms designed to allocate radio resources in a way that maximizes network performance while minimizing interference. Taken as alternate parameter is Laser communication, optical networks, satellite optical communication, vibrations, satellite networks. Taken as is solar radiation power, thermal bending, micro meteorite impact, solar and lunar gravity, earth oblations method. satellite optical communication has reached near 2000 data set compare other data set. The operation of wireless communication networks depends on radio resource management (RRM). Wireless networks would have interference, congestion, and a lacklustre level of service if effective RRM procedures weren't used. RRM is therefore a key component in ensuring that wireless communication systems can provide users with dependable and high-quality services.

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9

Nonchev, Stanislav, and Mikko Valkama. "ADVANCED RADIO RESOURCE MANAGEMENT FOR MULTI ANTENNA PACKET RADIO SYSTEMS." International Journal of Wireless & Mobile Networks 2, no. 2 (May 10, 2010): 1–14. http://dx.doi.org/10.5121/ijwmn.2010.2201.

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10

Gerasimenko, Mikhail, Dmitri Moltchanov, Roman Florea, Sergey Andreev, Yevgeni Koucheryavy, Nageen Himayat, Shu-Ping Yeh, and Shilpa Talwar. "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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11

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 (July 29, 2013): 785–97. http://dx.doi.org/10.1002/ett.2687.

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12

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 resource management framework is proposed, where the radio resources of different physical networks are virtualized into a virtual resource pool and mobile virtual network operators (MVNOs) compete for virtual resources from the pool to provide service to users. A virtual radio resource allocation algorithm based on biological model is developed, considering system utility, fairness, and dynamics. Simulation results are provided to verify that the proposed virtual resource allocation algorithm not only converges within a few iterations, but also achieves a better trade-off between total utility and fairness than existing algorithm. Besides, it can also be utilized to analyze the population dynamics of system.

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13

Shyshatskyi, Andrii, Artur Melnyk, Oleksii Bondar, Oleksandr Petruk, Dmytro Chernyahivskiy, Mykola Kryvenko, Oleksandr Petrov, Serhii Kravchuk, Yuriy Shidlovsky, and Volodymyr Lukianets. "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) (February 28, 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 scientific problem is solved by substantiating the methodological principles of radio resource management of military radio communication systems in conditions of a priori uncertainty. During the research, the authors used the main provisions of the theory of queuing, the theory of automation, the theory of complex technical systems, as well as general scientific methods of cognition, namely analysis and synthesis. The novelty is that in the course of work: – the purpose of functioning of an operative management subsystem of a radio resource of military radio communication systems is formulated; – indicators and criteria of functioning efficiency of military radio communication systems are determined; – decomposition of the solution of this problem into problems depending on the signal and noise situation is carried out. An approach based on the hierarchical decomposition of the functional structure of networks, the behavior of which is described by stochastic differential (or difference) equations of the high dimension state, into a number of interconnected but simpler functional structures is used for the functional description of military radio communication systems. It will allow to make a decomposition of the state of the military radio system and increase the efficiency of decision-making on adjusting the modes of operation and parameters of the military radio system in real time. The research results should be used at the stage of operational management of parameters and modes of these systems operation.

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14

Shaikh, Alam N., and S. K. Bodhe. "Comparison of Radio Resource Management in GSM." International Journal of Distributed and Parallel systems 2, no. 6 (November 30, 2011): 153–65. http://dx.doi.org/10.5121/ijdps.2011.2614.

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15

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

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16

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

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17

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 (July 2004): 375–89. http://dx.doi.org/10.1002/ett.987.

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18

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 (March 9, 2007): 151–67. http://dx.doi.org/10.1002/bltj.20201.

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19

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 (December 27, 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 the edge key resources similar to the available bandwidth in network communication channels. Aiming to understand the relationship between resource management, virtualization, and the dense 5G access and fronthaul with an emphasis on converged radio and optical communications, this article presents a review of how resource management solutions have dealt with optimizing millimeter wave radio and optical resources from an autonomic network management perspective. A research agenda is also proposed by identifying current state-of-the-art solutions and the need to shift all the convergent issues towards building an advanced resource management mechanism for beyond-5G.

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20

Bouleanu, Iulian, Dorin Alexandrescu, and Mircea Bora. "Radio Frequency Co-Site Management." International conference KNOWLEDGE-BASED ORGANIZATION 21, no. 3 (June 1, 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 using a large number of emission and reception sources for means of communication and non-communication in a small area. Locating several emission sources in the same site leads to different types of disturbing signals: emissions outside the bandwidth, harmonics and intermodulation. The article categorizes and describes these sources, presents the results of measurements distinguishing them, as well as the results of implementing some protective measures. Finally, the authors suggest a software solution for the local distribution of frequency resources.

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21

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 (May 15, 2011): 1228–33. http://dx.doi.org/10.3923/itj.2011.1228.1233.

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22

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 (January 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 control scheme that allows the CRN Base Station (BS) to delegate some of the management responsibilities to the network users. The main objective of the proposed framework is to reduce the CR users' reliance on the CRN BS and physical network resources while improving the network performance by reducing the control overhead.

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23

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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24

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 (October 2017): 166–74. http://dx.doi.org/10.1109/mwc.2017.1600220wc.

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25

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 (February 2007): 20–26. http://dx.doi.org/10.1109/mwc.2007.314547.

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26

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 among users.

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27

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 (January 28, 2008): 169–76. http://dx.doi.org/10.5392/jkca.2008.8.1.169.

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28

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

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29

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 (February 2014): 70–79. http://dx.doi.org/10.1109/mwc.2014.6757899.

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30

Kulkarni, Parag, Woon Hau Chin, and Tim Farnham. "Radio resource management considerations for LTE Femto cells." ACM SIGCOMM Computer Communication Review 40, no. 1 (January 7, 2010): 26–30. http://dx.doi.org/10.1145/1672308.1672314.

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31

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 (April 2019): 3014–23. http://dx.doi.org/10.1109/jiot.2018.2878511.

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32

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

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33

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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34

Gozalvez, J., M. C. Lucas-Estañ, and J. Sanchez-Soriano. "Joint radio resource management for heterogeneous wireless systems." Wireless Networks 18, no. 4 (December 29, 2011): 443–55. http://dx.doi.org/10.1007/s11276-011-0410-3.

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35

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 (August 2016): 6636–50. http://dx.doi.org/10.1109/tvt.2015.2479248.

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36

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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37

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

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38

Rodoshi, Rehenuma Tasnim, Taewoon Kim, and Wooyeol Choi. "Resource Management in Cloud Radio Access Network: Conventional and New Approaches." Sensors 20, no. 9 (May 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 management in C-RAN. This study performs a comprehensive survey on the state-of-the-art resource management techniques that have been proposed recently for this architecture. The resource management techniques are categorized into computational resource management (CRM) and radio resource management (RRM) techniques. Then both of the techniques are further classified and analyzed based on the strategies used in the studies. Remote radio head (RRH) clustering schemes used in CRM techniques are discussed extensively. In this research work, the investigated performance metrics and their validation techniques are critically analyzed. Moreover, other important challenges and open research issues for efficient resource management in C-RAN are highlighted to provide future research direction.

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39

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) (December 30, 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 special-purpose radio communication systems based on the evolutionary approach are solved. In the course of the research, the authors of the work used the main provisions of the theory of artificial intelligence, the theory of automation, the theory of complex technical systems, as well as general scientific methods of cognition, namely analysis and synthesis. The proposed methodological approach was developed taking into account the practical experience of the authors of this work during military conflicts of the last decade. The research results will be useful for: – development of new radio resource management algorithms; – substantiation of recommendations for improving the efficiency of radio resource operational management; – analysis of the radio-electronic situation during the conduct of hostilities (operations); – when creating promising technologies for increasing the efficiency of radio resource operational management; – assessment of the adequacy, reliability, sensitivity of the scientific and methodological apparatus for the operational management of the radio resource; – development of new and improvement of existing radio resource management models. Directions for further research will be aimed at developing a methodology for intelligent control of the radio resource of special-purpose radio communication systems.

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40

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 emphasize on some certain kinds of services and degrade the performance of others. Moreover, the wireless network is not able to detect collision and monitor the channel status as is the wire line network, which makes it more difficult to design the management scheme. This paper introduces and analyzes current main technologies of radio resource management, discusses the key technical points on designing the scheme, and then offers a new proposal of uplink radio resource allocation scheme in broadband systems. Based on the detailed illustration of the algorithm, a simulation system is established to verify it, which is adapted to the typical situation of broadband wireless communication systems. The theory analysis and the simulation results show that the new method successfully resolves the uplink bandwidth allocation problem for the simultaneous multiple services, satisfies the different requirements of delay and bandwidth of different services, and also reduces the protocol wasting and transmission delay.

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41

Delaney, James, Steve Dowey, and Chi-Tsun Cheng. "Reinforcement-Learning-Based Robust Resource Management for Multi-Radio Systems." Sensors 23, no. 10 (May 17, 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 multi-radio platforms and wireless devices with multiple and diverse transceiver technologies to produce robust and reliable links through the adaptive control of available transceivers. In this work, the term ‘robust’ refers to communications that can be maintained despite changes in the environmental and radio conditions, i.e., during periods of interference caused by non-cooperative actors or multi-path or fading conditions in the physical environment. In this paper, a multi-objective reinforcement learning (MORL) framework is applied to address a multi-radio selection and power control problem. We propose independent reward functions to manage the trade-off between the conflicting objectives of minimised power consumption and maximised bit rate. We also adopt an adaptive exploration strategy for learning a robust behaviour policy and compare its online performance to conventional methods. An extension to the multi-objective state–action–reward–state–action (SARSA) algorithm is proposed to implement this adaptive exploration strategy. When applying adaptive exploration to the extended multi-objective SARSA algorithm, we achieve a 20% increase in the F1 score in comparison to one with decayed exploration policies.

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42

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 (August 26, 2018): 2525–36. http://dx.doi.org/10.3233/jifs-182255.

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43

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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44

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

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45

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 (October 2017): 81–87. http://dx.doi.org/10.1109/mwc.2017.1700043.

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46

Arshad, Kamran, Richard Mackenzie, Ulrico Celentano, Arpad Drozdy, Stephanie Leveil, Juan Rico, Arturo Medela, and Christophe Rosik. "Resource management for QoS support in cognitive radio networks." IEEE Communications Magazine 52, no. 3 (March 2014): 114–20. http://dx.doi.org/10.1109/mcom.2014.6766095.

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47

Wang, Dingliang, Zhizhong Ding, Yin Chen, and Younan Duan. "LTE Radio Resource Management Conformance Testing: Exploration and Implementation." Journal of Communications 9, no. 11 (2014): 867–75. http://dx.doi.org/10.12720/jcm.9.11.867-875.

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48

Salkintzis, A. K. "Radio resource management in cellular digital packet data networks." IEEE Personal Communications 6, no. 6 (1999): 28–36. http://dx.doi.org/10.1109/98.813820.

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AlQahtani, Salman A. "Radio Resource Management Scheme for Multi-Agency TEDS Networks." Arabian Journal for Science and Engineering 38, no. 12 (July 24, 2013): 3321–30. http://dx.doi.org/10.1007/s13369-013-0628-8.

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Soares, Armando, Nuno Souto, João C. Silva, Patrícia Eusébio, and Américo Correia. "Effective Radio Resource Management for MBMS in UMTS Networks." Wireless Personal Communications 42, no. 2 (October 10, 2006): 185–211. http://dx.doi.org/10.1007/s11277-006-9173-z.

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