• Smart Media Journal
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• v.13 no.4
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• pp.33-48
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• 2024

With the advent of 5G, characterized by Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communications (URLLC), and Massive Machine Type Communications (mMTC), efficient network management and service provision are becoming increasingly critical. This paper proposes a novel approach to address key challenges of 5G networks, namely ultra-high speed, ultra-low latency, and ultra-reliability, while dynamically optimizing network slicing and resource allocation using machine learning (ML) and deep learning (DL) techniques. The proposed methodology utilizes prediction models for network traffic and resource allocation, and employs Federated Learning (FL) techniques to simultaneously optimize network bandwidth, latency, and enhance privacy and security. Specifically, this paper extensively covers the implementation methods of various algorithms and models such as Random Forest and LSTM, thereby presenting methodologies for the automation and intelligence of 5G network operations. Finally, the performance enhancement effects achievable by applying ML and DL to 5G networks are validated through performance evaluation and analysis, and solutions for network slicing and resource management optimization are proposed for various industrial applications.

• Korean Journal of Optics and Photonics
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• v.16 no.3
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• pp.182-190
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• 2005

The fiber Raman amplifier(FRA) is a distinctly advantageous technology. Due to its wider, flexible gain bandwidth, and intrinsically lower noise characteristics, FRA has become an indispensable technology of today. Various FRA modeling methods, with different levels of convergence speed and accuracy, have been proposed in order to gain valuable insights for the FRA dynamics and optimum design before real implementation. Still, all these approaches share the common platform of coupled ordinary differential equations(ODE) for the Raman equation set that must be solved along the long length of fiber propagation axis. The ODE platform has classically set the bar for achievable convergence speed, resulting exhaustive calculation efforts. In this work, we propose an alternative, highly efficient framework for FRA analysis. In treating the Raman gain as the perturbation factor in an adiabatic process, we achieved implementation of the algorithm by deriving a recursive relation for the integrals of power inside fiber with the effective length and by constructing a matrix formalism for the solution of the given FRA problem. Finally, by adiabatically turning on the Raman process in the fiber as increasing the order of iterations, the FRA solution can be obtained along the iteration axis for the whole length of fiber rather than along the fiber propagation axis, enabling faster convergence speed, at the equivalent accuracy achievable with the methods based on coupled ODEs. Performance comparison in all co-, counter-, bi-directionally pumped multi-channel FRA shows more than 102 times faster with the convergence speed of the Average power method at the same level of accuracy(relative deviation < 0.03dB).

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.8B
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• pp.691-700
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• 2006

Based on the destination IP address of incoming packets, the Internet routers determine next hops and forward packets toward final destinations through If address lookup. The bandwidth of communication links increases exponentially fast as well as the routing table size grows significant as the number of single host networks attached to the Internet increases. Since packets should be processed at wire-speed, the increased link speed reduces the processing time of a packet in routers, and hence more efficient and fast IP address lookup algorithms and architectures are required in the next generation routers. Most of the previous IP lookup schemes compare routing prefixes of shorter length first with a given input IP address. Since IP address lookup needs to find the most specific route of the given input, search continues until the longest matched prefix is found while it keeps remembering the current test matching prefix. In this paper, based on binary search on prefix length, we proposed a new IP address lookup algorithm which compares longer prefixes first. The proposed scheme is consisted of multiple tries with prefixes on leaves only. The trie composed of the longest prefixes is primarily searched whether there is a match with the given input. This processing is repeated for the trio of the next longer prefixes until there finds a match. Hence the proposed algorithm provides the fast search speed. The proposed algorithm also provides the incremental update of prefixes while the previous binary search on length scheme does not provide the incremental update because of pre-processing requirement. In this paper, we performed extensive simulations and showed the performance comparisons with related works.