Graph neural networks for sampling-invariant embeddings of organized signal sets

2026-09-28

AI Summary

This study addresses the challenge of uniformly processing heterogeneously sampled signals in sensor networks by proposing a graph neural network-based sampling-invariant embedding method. Through complex-valued radio frequency signal encoding and embedding learning, the proposed approach projects heterogeneously sampled signals under arbitrary topologies into a fixed-dimensional vector space. This effectively eliminates the influence of sampling discrepancies and enables topology-aware signal processing. Experimental results on synthetic signal datasets demonstrate that the method significantly enhances discriminative capability and successfully achieves waveform separation free from sampling bias, thereby overcoming the constraints inherent in traditional signal processing paradigms.

Abstract

Sensor networks and radars can deliver signals as organized sets, e.g. ordered signals, signals describing range cells within a grid or signals perceived as graph nodes. Within such sets, individual signals may be characterized by distinct sampling parameters. This paper investigates organized signal sets neural network encoders. In the context of this work, the purpose of such encoders is to project heterogeneously sampled signal sets into an arbitrary fixed-size vectors space. This new representation space is designed so that signal sets can be processed as vectors rid of sampling differences to allow for arbitrary topology-aware processing with no signal processing constraints. Within this representation space designed to reduce the influence of heterogeneous sampling parameters, the relevance of signal sets representations is evaluated by considering signal sets discrimination potential with a focus on waveforms separation. The encoding and embeddings discrimination experiments conducted rely exclusively on synthetic complex-valued radiofrequency signals.

Problem

• Graph Neural Networks
• Organized Signal Sets
• Sampling-invariant Embeddings
• Heterogeneous Sampling
• Waveform Separation

Innovation

• Graph Neural Networks
• Sampling-invariant Embeddings
• Organized Signal Sets
• Heterogeneous Sampling
• Complex-valued RF Signals