Smart Planning, Design, and Optimization of Mobile Networks Ecosystem Using AI-Enhanced Atoll Software

This chapter explores the importance of new smart approaches to planning, designing and optimizing the mobile network ecosystem. The paper focuses on the use of the Atoll planning software system developed by Forsk, which has significant potential for effective mobile network management. By analyzing various aspects of network planning, such as coverage, capacity, optimization, and resource management, it is shown that Atoll can successfully solve these problems. The software enables coverage simulation, equipment configuration, and network evolution estimation. However, the chapter also points out the lack of a built-in artificial intelligence (AI) module in Atoll and the need for such a module to automate data analysis and planning processes. We propose to use mobile traffic monitoring reports from sFlow software to develop a training set for the traffic volume forecasting module and integrate it into Atoll. A comprehensive analysis of actual mobile network traffic is conducted to effectively adapt to fluctuations in traffic volumes resulting from diverse factors. Next, we discuss the proposal to integrate artificial intelligence (AI) into the Atoll software to achieve more intelligent planning and optimization of mobile networks. Particular attention is paid to the development of an AI module based on the LSTM algorithm for accurate real-time traffic volume analysis. Our main novelty in this work was to make the existing LSTM mobile traffic forecasting model more suitable for real traffic volumes and provide more accurate forecasts specific to the Ukrainian region. The proposed LSTM traffic forecasting model showed higher accuracy (5.01% increase) and significantly improved forecasting speed (36% decrease) compared to the existing model. These enhancements enable operators to access more precise and quicker network load forecasts, promoting efficient resource management, heightened productivity, and an improved user experience.