A novel throughput mapping method for DC-HSDPA systems based on ANN. Issue 2 (February 2017)

• Record Type: Journal Article
• Authors: Kurnaz, Çetin; Engiz, Begüm; Esenalp, Murat
• Is Part Of: Neural computing & applications. Volume 28:Issue 2(2017)
• Page Start: 265
• Page End: 274
• Publication Date: 2017-02
• Subjects: DC-HSDPA -- User throughput -- Real field measurements -- Multiple linear regression -- ANFIS -- MLP -- GRNN; Neural networks (Computer science) -- Periodicals; Neural circuitry -- Periodicals; Artificial intelligence -- Periodicals; Neural Networks (Computer) -- Periodicals; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux nerveux -- Périodiques; Intelligence artificielle -- Périodiques; 006.32
• DOI: 10.1007/s00521-015-2054-1
• Languages: English
• ISSNs: 0941-0643

Abstract

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.