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

Abstract

In the 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 modelaided deep learning technique for path loss prediction at 7 GHz, as a representative frequency within the 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 learningbased 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.

Performance of Path Loss Models over Mid-Band and High-Band Channels for 5G Communication Networks: A Review

“…At the same time, some disagreed with the empirical models. For example, the NN model outperformed the empirical models, including the FI, CI, and 3GPP models in [93], which had a minimum RMSE value of 4.5 dB, as shown in Figure 7. Meanwhile, the independent machine learning models performed excellently, like the CNN model with an RMSE value of 8.59 dB at 28 GHz, the RF model with an RMSE value of 6.1 dB at 28 GHz, and the RNN model with an RMSE value of 2.4 dB at 26.4 GHz.…”

“…In [93], the authors conducted path loss prediction at 7 GHz within an urban environment by employing a model-assisted deep learning approach. Their proposed model utilizes a distinct set of input features, encompassing both fundamental and engineered attributes.…”

“…The DNN model outperformed the rest of the models with the minimum RMSE value. Meanwhile, the supremacy of the machine learning model over empirical models is showcased in [84], where the DNN model outperformed the ABG and CI models at 3.5 GHz with the minimum RMSE value of 4.84 dB, and in [93], where the NN model outperformed the FI and CI empirical models with an RMSE value of 4.3 dB at 7 GHz.…”