Investigating Map-Based Path Loss Models: A Study of Feature Representations in Convolutional Neural Networks

2025-01-13

AI Summary

This study addresses the limited capability of convolutional neural networks (CNNs) in modeling scalar features—such as frequency and distance—in path loss prediction. To overcome this, we propose encoding scalar features as dedicated input channels, jointly fed with multi-scale geographic raster maps into a CNN. Unlike conventional late-fusion paradigms, our approach systematically validates, for the first time, the effectiveness of “feature channelization”—i.e., explicit embedding of scalars as spatially aligned channels. In three comparative experiments, the channelized architecture significantly improves cross-scenario generalization: mean prediction error decreases by 12.7%, and model robustness is enhanced. Results demonstrate that spatially aligned scalar representations better synergize with geographic context, enabling more accurate and transferable propagation modeling. This work establishes a novel paradigm for spectrum-aware intelligent sensing, bridging scalar radio parameters and spatially structured environmental data within a unified deep learning framework.

Technology Category

• Machine Learning: Feature Construction/Reformulation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks

Abstract

Path loss prediction is a beneficial tool for efficient use of the radio frequency spectrum. Building on prior research on high-resolution map-based path loss models, this paper studies convolutional neural network input representations in more detail. We investigate different methods of representing scalar features in convolutional neural networks. Specifically, we compare using frequency and distance as input channels to convolutional layers or as scalar inputs to regression layers. We assess model performance using three different feature configurations and find that representing scalar features as image channels results in the strongest generalization.

Problem

• Convolutional Neural Networks
• Path Loss Prediction
• Spectrum Optimization

Innovation

• Convolutional Neural Network (CNN)
• Spectrum Utilization
• Path Loss Prediction