Mobile Soft Switch Traffic Prediction using Polynomial Neural Networks

Abstract

This work investigates busy hour traffic demand pattern of mobile soft switch (MSS) over a period of two years and propose the application of Group Method of Data Handling (GMDH) polynomial neural network for seven-days to three-month ahead busy hour (BH) traffic forecasting for effective optimization of network resources. Busy hour call attempt (BHCA) utilization and A-interface utilization key performance indicators (KPI) are used as inputs into GMDH prediction model and BH traffic as model target. The performance of the model was evaluated based on three statistical performance indices: mean absolute percentage error (MAPE), root mean square percentage error (RMSPE) and goodness of fit (R2) values. Experimental results show that R2 value as high as 96% was achieved with the proposed model for both short-term and mid-term forecasting. The GMDH model proves an effective tool for accurate prediction of traffic demand and hence, proper optimization of GSM/GPRS MSS network resources.