Deep Learning for Path Loss Prediction at 7 GHz in Urban Environment

In the
$\mathrm {6~GHz}$
spectrum sharing band, unlicensed devices are managed by automated frequency coordination (AFC) systems to protect incumbent services from interference. Thus, it is important to select accurate propagation models for interference calculation and analysis. This paper utilizes a model-aided deep learning technique for path loss prediction at
$\mathrm {7~GHz}$
, as a representative frequency within the
$\mathrm {6~GHz}$
band, in an urban environment. The proposed model is a hybrid model, which leverages both domain expert knowledge from a physics-based general-purposed channel model as well as the learning-based capability from a neural network, for path loss prediction. The model is trained and tested using sufficient-quantity and high-quality real propagation measurement data collected in four locations in an urban environment. Numerical results show that the deep learning model provides a better prediction performance than most empirical models. Furthermore, the feasibility of proposed model generalization to new locations after fine-tuning is examined.