White Papers & Articles


A/I Wireless Signal Identification and Analysis Technical White Paper

Radio frequency (RF) signal analysis is becoming essential for various industries as non-interrupted wireless connectivity becomes instrumental to operations. HPE and DeepSig have developed a solution which leverages artificial intelligence (AI) to detect and analyze wireless signals.


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Using OmniSIG SDK to Create a Drone Detection Model

Creating a custom neural network model for drone detection will enable you to perform signal classification on wireless signals emitted from small commercial drones.


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Mil-Embedded Publishes Article Co-Authored by DeepSig

The well known publication Military Embedded Systems has published an article about the application of artificial intelligence to wireless systems, with a specific focus on military communications and radio systems. The article was co-authored with Ettus Research.

The article can be found in the Nov/Dec 2018 print edition of Mil-Embedded, and on the publication website, here: http://mil-embedded.com/articles/ai-military-systems/


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NVIDIA DevBlog Post on Deep Learning for Wireless Communications

Read more on the NVIDIA blog post

The complexity of wireless system design is continually growing. Communications engineering strives to further improve metrics like throughput and interference robustness while simultaneously scaling to support the explosion of low-cost wireless devices. These often-competing needs makes system complexity intractable. Furthermore, algorithmic and hardware components are designed separately, then optimized, and integrated to form complete systems. This approach makes globally optimizing the end-to-end communications link extremely difficult, if not impossible.

DeepSig overcomes this complexity barrier by designing neural networks that learn how to effectively communicate, even under harsh impairments. To accomplish this, we leverage our background in radio and signal processing, recent developments in deep learning, and technology from NVIDIA such as GPU hardware and software libraries optimized for machine learning. Our work in learned communications demonstrates that machines easily match the performance of human-designed systems in simple scenarios, as shown in Figure 1. In more complex scenarios, a deep learning-based system can dramatically outperform existing approaches by learning a physical layer (PHY) inherently optimized for the radio hardware and channel.

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