A speculative DL classifier validated by GLRT on spatially robust second-order statistics provides adversarially resilient array processing.
Convolutional Radio Modulation Recognition Networks
2 Pith papers cite this work. Polarity classification is still indexing.
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abstract
We study the adaptation of convolutional neural networks to the complex temporal radio signal domain. We compare the efficacy of radio modulation classification using naively learned features against using expert features which are widely used in the field today and we show significant performance improvements. We show that blind temporal learning on large and densely encoded time series using deep convolutional neural networks is viable and a strong candidate approach for this task especially at low signal to noise ratio.
verdicts
UNVERDICTED 2representative citing papers
SurReal architecture applies weighted Fréchet mean convolution and distance-based FC layers to complex data, improving accuracy on MSTAR (94% to 98%) and RadioML with 8-10% of baseline model size.
citing papers explorer
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A Speculative GLRT-Backed ApproachRobust Deep Learning-Based Array Processing
A speculative DL classifier validated by GLRT on spatially robust second-order statistics provides adversarially resilient array processing.
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SurReal: Fr\'echet Mean and Distance Transform for Complex-Valued Deep Learning
SurReal architecture applies weighted Fréchet mean convolution and distance-based FC layers to complex data, improving accuracy on MSTAR (94% to 98%) and RadioML with 8-10% of baseline model size.