Deep Learning based Fusion Model for Multivariate LTE Traffic Forecasting and Optimized Radio Parameter Estimation

• Published in: IEEE access 2023-01, Vol.11, p.1-1
• Main Authors: Nabi, Syed Tauhidun, Islam, Md Rashidul, Alam, Md. Golam Rabiul, Hassan, Mohammad Mehedi, Alqahtani, Salman A., Aloi, Gianluca, Fortino, Giancarlo
• Format: Article
• Language: English
• Subjects: Algorithms; Benchmarks; Cellular communication; Cellular networks; Deep learning; Forecasting; Long Term Evolution; LTE Networks; Machine learning; Machine Learning in Networking; Mathematical models; Mobile Network Capacity; Multivariate analysis; Optimization; Parameter estimation; Physical Resource Block; Predictive models; Quality of service; Quality of service architectures; Resource management; Telecommunication traffic; Traffic models; Traffic Prediction; User experience; Utilization
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2023.3242861

With the evaluation of cellular network internet data traffic, forecasting and understanding traffic patterns become the critical objectives for managing the network-designed Quality of Service (QoS) benchmark. For this purpose, cellular network planners often use different methodologies for predicting data traffic. However, traditional traffic forecasting approaches are erroneous. As well as most of the time, traditional traffic forecasts are high-level or a generously large regional cluster level. Also, eNodeB-level utilization with concerning traffic forecasting is not readily available. As a result, user experience degradation or unnecessary network expansion is triggered based on the traditional method. This research focuses on extensive 6.2 million real network time series LTE data traffic and other associate parameters, including eNodeB-wise PRB utilization, which mainly focuses on building a traffic forecasting model with the help of multivariate feature inputs and deep learning algorithms. The state-of-the-art Deep Learning algorithm-based fusion model (in the combination of LSTM, BiLSTM, and GRU) enables traffic forecasting at a granular eNodeB-level and also provides eNodeB-wise forecasted PRB utilization. In this research R 2 score value for the proposed fusion model is 0.8034, which outperforms traditional models. Apart from the PRB utilization, QoS threshold was devised as 70% from a real network experience to trigger soft parameter tuning decisions. Based on the forecasted PRB utilization, this research proposed a unique algorithm that estimates eNodeB-level soft capacity parameter optimization for a short-term step-up solution or long-term network expansion to ensure a guaranteed QoS benchmark.