Machine learning (ML) provides effective means to learn from spectrum data and solve
complex tasks involved in wireless communications. Supported by recent advances in …

Transfer learning is a pervasive technology in computer vision and natural language
processing fields, yielding exponential performance improvements by leveraging prior …

Automatic modulation classification is of crucial importance in wireless communication
networks. Deep learning based automatic modulation classification schemes have attracted …

Automatic modulation classification (AMC) is of crucial importance in the sixth generation
wireless communication networks. Deep learning (DL)-based AMC schemes have attracted …

Space edge computing has unique considerations (eg, size, power, space radiation, etc.)
that limit the performance capabilities of achievable onboard processing. Due to these …

Performance of radio frequency machine learning (RFML) models for classification tasks
such as specific emitter identification (SEI) and automatic modulation classification (AMC) …

Spectrum prediction is of crucial importance for realizing the cognitive Internet of Things to
tackle the spectrum scarcity problem. Deep-learning-based spectrum prediction methods …

While Radio Frequency Machine Learning (RFML) is expected to be a key enabler of future
wireless standards, a significant challenge to the widespread adoption of RFML techniques …

We propose a framework, RiftNeXtTM, to perform radio frequency (RF) scene context
change detection and classification with Expert driven neural explainability. Our approach …

In evolving technology, attacks on medical devices are optimized due to the driving force of
AI, computer vision, mixed reality, and the internet of things (IoT). Optimizing cybersecurity …