Dual-Input Multi-Layered Attention Model for Enhanced Path Loss Prediction

Path loss prediction is crucial for optimizing base station placement in cellular networks. Traditional methods rely on extensive field testing, which is time-consuming and resource-intensive. Machine learning (ML)-based approaches offer an alternative, yet most existing models use unimodal systems, limiting predictive accuracy. To address this, we propose a Bimodal Path Loss Prediction System that integrates environmental data with visual information extracted from satellite images. We introduce the Dual-Input Integrative Attention Model (DIIAM), a multi-layered architecture designed for improved path loss prediction. DIIAM consists of three key layers: Dual-Input Feature Extraction Layer (DIFEL), Feature Weighted Attention Layer (FWAL), and Learning Layer (LL). DIFEL extracts environmental features using data imputation, normalization, and statistical feature selection, while visual features are obtained using the ResNet50 transfer learning model. FWAL applies an attention mechanism to enhance feature relevance, and LL employs six different learning models—SVR, RFR, BPNN, LSTM, BiLSTM, and GRU—to effectively capture complex feature relationships. Evaluated on four publicly available datasets, DIIAM achieves an average RMSE of approximately 1.5 dB, outperforming state-of-the-art methods. The results demonstrate the effectiveness of integrating environmental and visual data for path loss prediction, offering a more accurate and computationally efficient alternative to traditional and unimodal ML approaches.