An optimized neural network for monitoring Key Performance Indicators in HSDPA

Author

Pierucci, Laura ; Romoli, Alessandra ; Fantacci, Romano ; Micheli, Davide

Author_Institution

Univ. of Florence, Florence, Italy

fYear

2010

Firstpage

2041

Lastpage

2045

Abstract

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.

Keywords

cellular radio; neural nets; quality of service; telecommunication computing; wireless channels; HSDPA; broadband services; high speed downlink packet access; key performance indicators; learning vector quantization; optimized neural network; quality of service; radio channel quality; universal mobile telecommunication system; Artificial neural networks; Mobile communication; Multiaccess communication; Neurons; Radiation detectors; Spread spectrum communication; Throughput; Channel Quality; HSDPA; Key Performance Indicators; Neural Networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Personal Indoor and Mobile Radio Communications (PIMRC), 2010 IEEE 21st International Symposium on

Conference_Location

Instanbul

Print_ISBN

978-1-4244-8017-3

Type

conf

DOI

10.1109/PIMRC.2010.5671580

Filename

5671580