A multiple linear regression-based machine learning model for received signal strength prediction of multiband applications Online publication date: Fri, 09-Feb-2024
by M. Benisha; V. Thulasi Bai
International Journal of Mobile Communications (IJMC), Vol. 23, No. 2, 2024
Abstract: 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.
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