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. ©2010 IEEE.
An optimized neural network for monitoring key performance indicators in HSDPA / Laura Pierucci Ieee, Member; Alessandra, Romoli; Micheli, Davide. - ELETTRONICO. - (2010), pp. 2041-2045. (Intervento presentato al convegno 2010 IEEE 21st International Symposium on Personal Indoor and Mobile Radio Communications, PIMRC 2010 tenutosi a Istanbul; Turkey nel 26 September 2010 through 30 September 2010) [10.1109/pimrc.2010.5671580].
An optimized neural network for monitoring key performance indicators in HSDPA
MICHELI, DAVIDE
2010
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. ©2010 IEEE.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
