Comparison of the Long-Term forecasting method of RSSI by Machine Learning

Abstract

In order to improve the efficiency of spectrum use, systems that share spectrum while avoiding interference between different systems are being investigated. In the millimeter-wave band, which is expected to be utilized in the future, the received power fluctuates due to quasi-static obstructions such as people and vehicles, but such temporal variations have not been taken into account in conventional methods. In this paper, we use a variety of machine learning algorithms for comparative evaluation to forecast the temporal fluctuations of radio propagation due to changes in the number of people and vehicles in order to achieve more dynamic spectrum access.