An Optimized Neural Network for monitoring Key Performance Indicators in HSDPA

被引：5

作者：

Pierucci, Laura ^{[1
]}

Romoli, Alessandra ^{[1
]}

Fantacci, Romano ^{[1
,2
]}

Micheli, Davide ^{[2
]}

机构：

[1] Univ Florence, VS Marta 3, Florence, Italy

[2] Telecom Italia, Rome, Italy

来源：

2010 IEEE 21ST INTERNATIONAL SYMPOSIUM ON PERSONAL INDOOR AND MOBILE RADIO COMMUNICATIONS (PIMRC) | 2010年

关键词：

Neural Networks; Key Performance Indicators; Channel Quality; HSDPA;

D O I：

10.1109/PIMRC.2010.5671580

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

HSDPA (High Speed Downlink Packet Access) is drawing great attention as the 3.5G technology capable of providing higher data rate packet switch services over Universal Mobile Telecommunication System (UMTS) to support broadband services like multimedia conferencing, VoIP, or high-speed internet access. The paper proposes the use of a Learning Vector Quantization (LVQ) Neural Network able to estimate the quality of service (QoS) across analysis of Key Performance Indicators (KPIs) and to provide automatically a possible classification of warnings related to the load status of HSDPA radio resources or to the bad radio channel quality condition.

引用

页码：2041 / 2045

页数：5

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