Next Generation Networks (NGNs)

Chao, Han‐Chieh; Zeadally, Sherali; Chen, Yuh‐Shyan; Martinez, Gregorio; Wang, Reen-Cheng

doi:10.1002/dac.1144

Cited by 7 publications

(6 citation statements)

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“…As a matter of fact, efficient utilization of the spectrum has become a need of next generation wireless networks [1]. As a matter of fact, efficient utilization of the spectrum has become a need of next generation wireless networks [1].…”

Section: Introductionmentioning

confidence: 99%

CCRA: channel criticality based resource allocation in cognitive radio networks

Kumar

Minz

2015

Int J Communication

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In cognitive radio networks, signal to interference plus noise ratio (SINR) is a quantity that is used to analyze the bounds of the capacity of a channel. This is the reason for SINR being one of the important parameters toward evaluating the performance of spectrum sharing in every network. To maximize the channel utilization in any network, the SINR of a channel should be considered to be within a threshold. This also leads to lesser power consumption, and the quality of service for the licensed users is maintained. Further, reduced SINR leads to an improvement in the quality of communication in the network. In this paper, a graph theoretic measure for the efficient utilization of channels in cognitive radio networks has been proposed and is named as channel criticality based resource allocation (CCRA). Using the SINR as the weight of the graph, a novel concept of channel criticality has also been introduced in this work. The proposed CCRA technique has also been compared with the existing interference aware channel assignment (IACA) technique in terms of the channel utilization. Through simulations, the CCRA has been observed to outperform the IACA scheme. The average channel utilization of the proposed CCRA was observed to have increased by 8%, when the secondary users were introduced in the network as compared with the IACA technique. Copyright Int. J. Commun. Syst. 2017; 30: e3012 V. KUMAR AND S. MINZThe betweenness of the link k for the source and destination pair .a; b/ is given by of 12 6

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Section: Introductionmentioning

confidence: 99%

CCRA: channel criticality based resource allocation in cognitive radio networks

Kumar

Minz

2015

Int J Communication

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“…As the Internet developed in recent decades, supports for mobility [1][2][3] and demands of content distribution have become more and more essential. To overcome such challenge, many researches [4][5][6][7][8][9][10] to conceive new potential next-generation Internet architectures have been presented. Named data networking (NDN) [4,11], sponsored by the National Science Foundation (NSF), is one such research.…”

Section: Introductionmentioning

confidence: 99%

Cooperative detection and protection for Interest flooding attacks in named data networking

Ding

Liu

Cho

et al. 2014

Int J Communication

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Summary Named data networking (NDN) is a new emerging architecture for future network, which may be a substitute of the current TCP/IP‐based network, for the content‐oriented data request mode becoming the future trend of development. The security of NDN has attracted much attention, as an implementation of next‐generation Internet architecture. Although NDN is immune to most current attack, it cannot resist the distributed denial of service like attack – Interest flooding attack (IFA) – effectively. IFA takes advantages of the forwarding mechanism of NDN, flooding a large number of malicious Interest packets at quite a high rate, and exploits the network resources, which may cause the paralysis of the network. Taking into account the severity of the destruction, we propose an algorithm to counter such new type of attack. We analyze three properties of IFA, and use them to judge and filter Interest packets. Vector space model and Markov model are used in our method to realize a cooperative detection. Meanwhile, we present the retransmission forwarding mechanism to ensure legitimate user request. The ndnSIM module of ns3 is used for the corresponding simulation, and results of the simulation will be given to show the effectiveness of our algorithm. Copyright © 2014 John Wiley & Sons, Ltd.

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“…The development and advancement in the wireless networks has posed a major challenge to the design and standardization of the Next Generation Mobile Networks (NGMN) [1] [2]. In this context, the goals are to reduce both the complexity of management tasks and the operational cost.…”

Section: Introductionmentioning

confidence: 99%

Self Organizing Networks: A Reinforcement Learning approach for self-optimization of LTE Mobility parameters

Tiwana

2014

Automatika

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Original scientific paperWith the evolution of broadband mobile networks towards LTE and beyond, the support for the Internet and Internet based services is growing. Self Organizing Network (SON) functionalities intend to optimize the network performance for the improved user experience while at the same time reducing the network operational cost. This paper proposes a Reinforcement Learning (RL) based framework to improve throughput of the mobile users. The problem of spectral efficiency maximization is modeled as co-operative Multi-Agent control problem between the neighbouring eNodeBs (eNBs). Each eNB has an associated agent that dynamically changes the outgoing Handover Margin (HM) to its neighbouring cells. The agent uses the RL technique of Fuzzy Q-Learning (FQL) to learn the optimal mobility parameter i.e., HM value. The learning framework is designed to operate in an environment with the variations in traffic, user positions and propagation conditions. Simulation results have shown the proposed approach improves the network capacity and user experiences in terms of throughput. Key words: Handover Margin, LTE, Reinforcement Learning, Fuzzy Q-Learning, SONSamoorganizirajuće mreže: Podržano učenje za optimizaciju LTE mobilnosti. Razvoj širokopojasne mobilne mreže prema LTE mrežama uvjetuje pojačani rast internetskih servisa i usluga. Samoorganizirajuće mreže namijenjene su optimizaciji performansi mreže s ciljem poboljšanja korisnikovog zadovoljstva i smanjenja troškova rada. U radu se predlaže pristup zasnovan na podržanom učenju kako bi se popravila propusnost mobilnog korisnika. Problem maksimizacije spektralne učinkovitosti modelira se kao kooperativni više agentski problem upravljanje izme u susjednihčvorova (eNBs). Svakičvor ima pridruženog agenta koji dinamički mijenja marginu primopredaje prema susjednimćelijama. Agent koristi tehniku neizrazitog Q učenja (FQL) kako bi naučio optimizirati parametre mreže. Učenje je organizirano za rad u uvjetima raznovrsnog prometa, korisničkih položaja i uvjeta propagacije. Simulacijski rezultati pokazuju kako predloženi pristup poboljšava kapacitet mreže i korisnički doživljaj u smislu propusnosti mreže.

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Next Generation Networks (NGNs)

Cited by 7 publications

References 0 publications

CCRA: channel criticality based resource allocation in cognitive radio networks

CCRA: channel criticality based resource allocation in cognitive radio networks

Cooperative detection and protection for Interest flooding attacks in named data networking

Self Organizing Networks: A Reinforcement Learning approach for self-optimization of LTE Mobility parameters

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