A multiple linear regression-based machine learning model for received signal strength prediction of multiband applications

In wireless communication, path loss prediction is of great impact to ensure service quality for users and performance optimisation. This requires a less complex and a more accurate path loss or received signal strength (RSS) prediction method. To deliver compliance, machine learning (ML) techniques have been considered. In this contribution, the principle behind ML-based RSS prediction and the procedure to correlate the antenna parameters well with the RSS value is presented for the designed multiband sub 6 GHz patch antenna, which can operate from 1 GHz to 6 GHz suitable for multiband applications. The regression-based ML method is used to train the model with simulated data and validated using Wi-Fi real-time RSS dataset. The same is extended for other frequency applications as well. From the predicted and measured values, it can be a best-suited model for the prediction of RSS thereby path loss for the future 5th generation wireless communications.