Dynamic resource allocation in cloud-radio access network using call detail record analysis

Abstract

We propose a solution based on call detail record (CDR) data analysis for cloud-radio access network (C-RAN) network optimization. First, we propose a data traffic prediction model in 3G and 4G networks using artificial intelligence (AI) models (long short-term memory (LSTM) and Bidirectional LTSM (BiLSTM)). Second, we propose a dynamic baseband units (BBU) resource allocation algorithm based on the obtained traffic prediction results to evaluate the rate of BBUs used as well as the average utilization rate of active BBUs in a C-RAN network. We used mean absolute error, root mean square error and mean absolute percentage error to evaluate the prediction model. The results obtained show that the best performance for estimating data traffic in 3G and 4G networks was obtained with the BiLSTM model, and is as follows: 1.143; 1.521; 2.47 percent for 4G, and for 3G, we have 0.2553; 0.3608 and 27.70 percent. Finally, evaluations with the predicted traffic dataset show that our framework provides up to 81% reduction in the number of BBUs used by the normal RAN. Moreover, active BBUs are exploited on average up to 88.34% of their capacity in a C-RAN compared to an average rate of 10.8% in a traditional RAN.