Smart Spectrum Recommendation Approach with Edge Learning for 5G and Beyond Radio Planning

Radio spectrum planning has become increasingly important, since the radio spectrum is a scarce resource. Moreover, the utilization of millimeter wave (mmWave) frequencies with fifth-generation (5G) standards has made radio planning more compelling. Considering their different strengths and weaknesses, it is essential to know when mmWave frequencies should be selected in radio planning. In this paper, an approach with edge learning is developed to provide smart spectrum recommendations on which frequency bands should be used for a region. Using the proposed approach, radio spectrum planning can be carried out more efficiently, especially for the frequency ranges of mmWave communications. The proposed approach is designed with a distributed structure, based on awareness of the environment and ambient intelligence. This approach can be performed for each transmission point considering the environment information of the related coverage area. As a result, radio spectrum planning can be conducted for an entire region with the proposed system. The results show that this study both enhances overall user satisfaction and provides reasonable recommendations to operators in the transition to mmWave usage. Thus, the developed approach can be utilized for 5G and beyond communications. Specifically, this methodology is based on applying supervised ML algorithms to a synthetically generated dataset, and the best model achieves around 80% classification accuracy, demonstrating the feasibility of the approach. These quantitative results confirm its practicality and provide a concrete baseline for future studies.