This standard describes how to generate datasets to train and test Machine-Learned Spectrum Awareness (MLSA) models that detect, classify, characterize, and/or identify radio frequency (RF) signals and signal emitters. The scope of this standard includes: methods for creating training and test datasets for MLSA models that are representative of real-world Dynamic Spectrum Access (DSA) and spectrum sharing scenarios, methods for using data augmentation techniques to introduce sufficient sample variation so that the MLSA model can generalize to real-world scenarios, methods for enhancing the training dataset with RF propagation channels and interference sources that are representative of real-world scenarios, specifications for how to structure and store MLSA datasets, methods for creating secure and performant MLSA models that operate on resource-constrained RF sensors and processors, and finally, criteria for evaluating the performance of MLSA models.
- Sponsor Committee
- COM/DySPAN-SC - Dynamic Spectrum Access Networks Standards Committee
Learn More About COM/DySPAN-SC - Dynamic Spectrum Access Networks Standards Committee - Status
- Active PAR
- PAR Approval
- 2021-03-25
Working Group Details
- Society
- IEEE Communications Society
Learn More About IEEE Communications Society - Sponsor Committee
- COM/DySPAN-SC - Dynamic Spectrum Access Networks Standards Committee
Learn More About COM/DySPAN-SC - Dynamic Spectrum Access Networks Standards Committee - Working Group
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MLSA - Machine Learning for RF Spectrum Awareness in DSA and Sharing Systems
Learn More About MLSA - Machine Learning for RF Spectrum Awareness in DSA and Sharing Systems - IEEE Program Manager
- Jennifer Santulli
Contact Jennifer Santulli - Working Group Chair
- Alexander Lackpour
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