Next Generation Networks (NGN) such as LTE and WiMAX offer higher spectral efficiency and data rates using new technologies such as femto cells, relay nodes etc. These networks are normally deployed for use in parallel with existing networks. This approach to network deployment complicates network operation and management, thus translating to higher capital and operational costs. In a bid to minimize these costs, self-organising operations were envisioned. Load balancing is a self-organising operation. It aims at ensuring an equitable distribution of users in the network. Several methods based on iterative techniques have been proposed and some adopted for load balancing. However, these iterative techniques are computationally intensive and use a limited number of parameters for load balancing. This work proposes two models developed from network simulations for load balancing. The two models are based on Adaptive Neuro-Fuzzy Inference System (ANFIS) designed using load indicators and key performance indicators
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Next Generation Networks (NGN) such as LTE and WiMAX offer higher spectral efficiency and data rates using new technologies such as femto cells, relay nodes etc. These networks are normally deployed for use in parallel with existing networks. This approach to network deployment complicates network operation and management, thus translating to higher capital and operational costs. In a bid to minimize these costs, self-organising operations were envisioned. Load balancing is a self-organising operation. It aims at ensuring an equitable distribution of users in the network. Several methods based on iterative techniques have been proposed and some adopted for load balancing. However, these iterative techniques are computationally intensive and use a limited number of parameters for load balancing. This work proposes two models developed from network simulations for load balancing. The two models are based on Adaptive Neuro-Fuzzy Inference System (ANFIS) designed using load indicators and key performance indicators 104 pp. Englisch. N° de réf. du vendeur 9783659192883
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Luka Matthew K.M.K. Luka received his B.Eng. in Electrical and Electronics Engineering from Federal University of Technology Yola in 2009, and an M.Eng. (ICT) from the Department of Electrical and Information Engineering, Covenant U. N° de réf. du vendeur 5138505
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Taschenbuch. Etat : Neu. Neuware -Next Generation Networks (NGN) such as LTE and WiMAX offer higher spectral efficiency and data rates using new technologies such as femto cells, relay nodes etc. These networks are normally deployed for use in parallel with existing networks. This approach to network deployment complicates network operation and management, thus translating to higher capital and operational costs. In a bid to minimize these costs, self-organising operations were envisioned. Load balancing is a self-organising operation. It aims at ensuring an equitable distribution of users in the network. Several methods based on iterative techniques have been proposed and some adopted for load balancing. However, these iterative techniques are computationally intensive and use a limited number of parameters for load balancing. This work proposes two models developed from network simulations for load balancing. The two models are based on Adaptive Neuro-Fuzzy Inference System (ANFIS) designed using load indicators and key performance indicatorsBooks on Demand GmbH, Überseering 33, 22297 Hamburg 104 pp. Englisch. N° de réf. du vendeur 9783659192883
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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Next Generation Networks (NGN) such as LTE and WiMAX offer higher spectral efficiency and data rates using new technologies such as femto cells, relay nodes etc. These networks are normally deployed for use in parallel with existing networks. This approach to network deployment complicates network operation and management, thus translating to higher capital and operational costs. In a bid to minimize these costs, self-organising operations were envisioned. Load balancing is a self-organising operation. It aims at ensuring an equitable distribution of users in the network. Several methods based on iterative techniques have been proposed and some adopted for load balancing. However, these iterative techniques are computationally intensive and use a limited number of parameters for load balancing. This work proposes two models developed from network simulations for load balancing. The two models are based on Adaptive Neuro-Fuzzy Inference System (ANFIS) designed using load indicators and key performance indicators. N° de réf. du vendeur 9783659192883
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Taschenbuch. Etat : Neu. ANFIS Models for Dynamic Load Balancing in 3GPP LTE | A Fresh Take on Load Balancing in NGN | Matthew K. Luka (u. a.) | Taschenbuch | 104 S. | Englisch | 2012 | LAP LAMBERT Academic Publishing | EAN 9783659192883 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu. N° de réf. du vendeur 106361584
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