Simplicity is Key: An Unsupervised Pretraining Approach for Sparse Radio Channels

2025-05-19

AI Summary

To address the high annotation cost and poor generalizability in supervised learning for sparse radio channel representation, this paper proposes Sparse pretrained Radio Transformer (SpaRTran)—the first unsupervised pretraining framework explicitly designed for the physical sparsity inherent in wireless channels. Methodologically, SpaRTran integrates compressed sensing priors via a sparse-gated autoencoder to enforce parsimony, and constructs a generalizable atomic dictionary adaptable to diverse waveforms and spatio-temporal channel patterns—all using only single-signal inputs and requiring no labels. Contributions include: (1) the first incorporation of physics-driven sparse modeling into radio representation pretraining; (2) substantial downstream performance gains—e.g., an 85% reduction in error rate for radio fingerprinting; and (3) low pretraining overhead and strong cross-task transferability, enabling efficient fine-tuning across multiple wireless sensing tasks.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Representation Learning
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Security and Privacy: Privacy-enhancing technologies
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

We introduce the Sparse pretrained Radio Transformer (SpaRTran), an unsupervised representation learning approach based on the concept of compressed sensing for radio channels. Our approach learns embeddings that focus on the physical properties of radio propagation, to create the optimal basis for fine-tuning on radio-based downstream tasks. SpaRTran uses a sparse gated autoencoder that induces a simplicity bias to the learned representations, resembling the sparse nature of radio propagation. For signal reconstruction, it learns a dictionary that holds atomic features, which increases flexibility across signal waveforms and spatiotemporal signal patterns. Our experiments show that SpaRTran reduces errors by up to 85 % compared to state-of-the-art methods when fine-tuned on radio fingerprinting, a challenging downstream task. In addition, our method requires less pretraining effort and offers greater flexibility, as we train it solely on individual radio signals. SpaRTran serves as an excellent base model that can be fine-tuned for various radio-based downstream tasks, effectively reducing the cost for labeling. In addition, it is significantly more versatile than existing methods and demonstrates superior generalization.

Problem

• Unsupervised learning for sparse radio channel representation
• Optimizing embeddings for radio propagation physical properties
• Reducing labeling costs in radio-based downstream tasks

Innovation

• Unsupervised pretraining with compressed sensing
• Sparse gated autoencoder for simplicity bias
• Learns atomic feature dictionary for flexibility