Active PAR

P1900.8

Standard for Training, Testing, and Evaluating Machine-Learned Spectrum Awareness Models

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
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Status
Active PAR
PAR Approval
2021-03-25

Working Group Details

Society
IEEE Communications Society
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Sponsor Committee
COM/DySPAN-SC - Dynamic Spectrum Access Networks Standards Committee
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Working Group
MLSA - Machine Learning for RF Spectrum Awareness in DSA and Sharing Systems
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IEEE Program Manager
Jennifer Santulli
Contact
Working Group Chair
Alexander Lackpour
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No Inactive-Withdrawn Standards
No Inactive-Reserved Standards
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