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A novel throughput mapping method for DC-HSDPA systems based on ANN
Kurnaz Cetin
Engiz Begum Korunur
Esenalp Murat
Neural Computing & Applications, 2017, 28(2): 265-274.
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.
DC-HSDPA; User throughput; Real field measurements; Multiple linear regression; ANFIS; MLP; GRNN
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