A novel throughput mapping method for DC-HSDPA systems based on ANN
NEURAL COMPUTING & APPLICATIONS, vol.28, no.2, pp.265-274, 2017 (SCI-Expanded, Scopus)
- Publication Type: Article / Article
- Volume: 28 Issue: 2
- Publication Date: 2017
- Doi Number: 10.1007/s00521-015-2054-1
- Journal Name: NEURAL COMPUTING & APPLICATIONS
- Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
- Page Numbers: pp.265-274
- Keywords: DC-HSDPA, User throughput, Real field measurements, Multiple linear regression, ANFIS, MLP, GRNN
- Ondokuz Mayıs University Affiliated: Yes
Abstract
In order to improve support for higher data rates, third-generation partnership project (3GPP) introduced dual-carrier high-speed downlink packet access (DC-HSDPA), which reaches up to 42-Mbps throughput with the use of two adjacent 5-MHz carriers in Release-8. Defining the dependence of throughput on prevailing channel parameters is crucial because a frequency-selective channel limits achieving these data rates. For this reason, DC-HSDPA throughput real field measurements were taken in different propagation environments by using the "TEMS Investigation" program. The evaluation of the measurements showed that one-parameter linear mapping methods, such as signal-to-interference ratio and channel quality indicator, are insufficient for characterizing user throughput. Therefore, this study will propose a novel mapping method with more than one variable. Although multiple linear regression gives a better normalized root-mean-square error, results have shown that frequently used artificial neural network-based mapping methods-such as those for adaptive network-based fuzzy inference system, multilayer perceptron, and generalized regression neural network (GRNN)-yield improved accuracy. From among these, user throughput can be best estimated with the use of GRNN for a commercial DC-HSDPA system, with approximately 93.3 % precision. The GRNN structure allows system designers to update system parameters to maximize user throughput.