Enhancing Open RAN Operations: The Role of Probabilistic Forecasting in Network Analysis

Resource provisioning plays a crucial role in effective resource management. As we move into the 6G era, technologies such as Open Radio Access Network (O-RAN) offer the opportunity to develop intelligent and interoperable cutting-edge solutions for qualitative management of the latest communication system. Previous works have mostly used single-point forecasts like Long-Short Term Memory (LSTM) for predicting resource requirements, which presents decision-makers with the problem of making informed decisions about resource allocation. On the other hand, probability-based forecasting techniques such as DeepAR, Transformer and Simple-Feed-Forward (SFF) offer new dimensions to the predictions by quantifying their uncertainties. This work shows the comprehensive comparison of single-point and probabilistic estimators and evaluates their effectiveness in predicting the actual number of Physical Resource Blocks (PRBs) needed in the context of O-RAN, especially for multi-tenant use cases. The results show the superiority of the probabilistic model in terms of various evaluation metrics. DeepAR achieves the highest accuracy, outperforming single-point and other probabilistic estimators. Based on these findings, a novel approach named Dynamic Percentile Adjustment Approach (DYNp) algorithm is proposed, which utilizes probabilistic forecasting for adaptive resource allocation. After extensive analysis, the numerical results show that the DYNp algorithm for DeepAR predictions reduces the Service Level Agreement (SLA) violation to 8% and the over-provisioning to 0.509 by dynamic percentile adaption. DYNp approach ensures that resources are allocated by efficiently handling over-and under-provisioning, making it suitable for real-time scenarios in O-RAN environments.