PathFinder: Advancing Path Loss Prediction for Single-to-Multi-Transmitter Scenario
Summary: arXiv:2512.14150v3 Announce Type: replace-cross
Radio path loss prediction (RPP) plays a pivotal role in optimizing 5G networks, which are essential for the development of Internet of Things (IoT), smart cities, and similar applications. Despite the advancements in this field, existing deep learning-based RPP methods exhibit significant drawbacks, particularly in their handling of environmental modeling and multi-transmitter scenarios.
Challenges in Current RPP Methods
In the pursuit of enhancing radio path loss predictions, researchers have identified three core challenges that current methodologies face:
- Passive Environmental Modeling: Many existing models overlook the critical role of transmitters and essential environmental features, thereby limiting their effectiveness.
- Overemphasis on Single-Transmitter Scenarios: Most methods focus predominantly on scenarios with a single transmitter, despite the prevalence of multi-transmitter environments in real-world applications.
- Neglect of Distribution Shifts: There is often an excessive focus on in-distribution performance, with insufficient attention given to the challenges posed by distribution shifts, particularly when the training and testing environments differ significantly.
Introducing PathFinder
To tackle these foundational issues, researchers have developed PathFinder, a novel architecture designed to actively model both buildings and transmitters through disentangled feature encoding. This innovative approach integrates a mechanism known as Mask-Guided Low-Rank Attention, which allows the model to independently focus on receiver and building regions, enhancing the accuracy of predictions in complex environments.
Innovative Training Strategies
In addition to its architectural advancements, PathFinder introduces a new training strategy called Transmitter-Oriented Mixup. This technique aims to bolster the robustness of the model during training by effectively integrating features from different transmitter scenarios. This is crucial for improving performance in multi-transmitter settings, which are often more representative of real-world conditions.
New Benchmark: Single-to-Multi-Transmitter RPP (S2MT-RPP)
To evaluate the extrapolation performance of PathFinder, a new benchmark has been established, referred to as Single-to-Multi-Transmitter RPP (S2MT-RPP). This benchmark is particularly focused on assessing the model’s ability to generalize from training on single-transmitter scenarios to testing in multi-transmitter environments, a critical aspect of real-world applications.
Experimental Results
Experimental evaluations demonstrate that PathFinder significantly outperforms existing state-of-the-art methods, particularly in challenging multi-transmitter scenarios. The results underscore the effectiveness of the proposed architectural innovations and training strategies in addressing the limitations of traditional RPP approaches.
Access to Code and Project Site
For those interested in further exploration, the code and project site for PathFinder are available at: PathFinder Project Site.