DeepSig, Inc.
DeepSig develops AI and machine-learning software and research for wireless communications. Primary focus areas include neural-network implementations of radio physical-layer functions for virtualized RAN, machine-learning spectrum sensing and RF classification, open-source CU/DU software for Open RAN, and AI-enhanced massive MIMO techniques. The company publishes historical RF datasets and model repositories for research under a Creative Commons non-commercial share-alike license.
Industries
N/A
Products
Neural receiver software for 5G vRAN
Software implementing a neural-network upper PHY for virtualized RAN deployments to improve channel estimation, equalization and uplink throughput compared with conventional PHY processing.
ML-driven spectrum sensing software
Real-time wideband spectrum sensing and analytics software for detection, classification and localization of RF emissions for operational and security use cases.
Model training and deployment studio for spectrum ML
Software environment for curating RF datasets, training models, and deploying spectrum-sensing ML models to edge and cloud targets.
Neural receiver software for 5G vRAN
Software implementing a neural-network upper PHY for virtualized RAN deployments to improve channel estimation, equalization and uplink throughput compared with conventional PHY processing.
ML-driven spectrum sensing software
Real-time wideband spectrum sensing and analytics software for detection, classification and localization of RF emissions for operational and security use cases.
Model training and deployment studio for spectrum ML
Software environment for curating RF datasets, training models, and deploying spectrum-sensing ML models to edge and cloud targets.
Services
Neural PHY integration for virtualized RAN
Integration of neural-network based upper-PHY software into virtualized RAN components to improve channel estimation, equalization and uplink throughput.
Real-time spectrum awareness and RF classification
Continuous ML-powered sensing and analytics to detect, classify and localize emitters for network optimization, interference mitigation and security monitoring.
Model training, validation and deployment platform for spectrum ML
Tooling and workflows for training, validating and deploying ML models for spectrum sensing and RF applications to edge and cloud targets.
Neural PHY integration for virtualized RAN
Integration of neural-network based upper-PHY software into virtualized RAN components to improve channel estimation, equalization and uplink throughput.
Real-time spectrum awareness and RF classification
Continuous ML-powered sensing and analytics to detect, classify and localize emitters for network optimization, interference mitigation and security monitoring.
Model training, validation and deployment platform for spectrum ML
Tooling and workflows for training, validating and deploying ML models for spectrum sensing and RF applications to edge and cloud targets.
Expertise Areas
- AI-native physical-layer communications
- Machine-learning spectrum awareness and RF classification
- Open RAN CU/DU software development and integration
- Massive MIMO and ML beamforming optimization
Key Technologies
- Deep neural networks for PHY
- ML-driven spectrum sensing
- Neural receiver algorithms for vRAN
- Massive MIMO beamforming algorithms