Virginia Patent of the Month – November 2023
DeepSig Inc. is directing machine learning capabilities to the world of wireless communications, leveraging neural networks to improve performance and capabilities.
In line with this mission, the company has introduced a novel method to revolutionize radio signal processing. The process integrates a nonlinear pre-distortion machine learning model into wireless devices, designed to correct radio signal distortion and interference with unprecedented efficiency.
The method starts by obtaining a transmit radio signal configured for wireless communication. What sets this approach apart is the integration of a nonlinear pre-distortion machine learning model into the wireless device. This model encompasses crucial parameters associated with various deployment scenarios, allowing it to adapt and optimize performance across different setups. One of the key features is the inclusion of nonlinear functions, strategically designed to correct radio signal distortion.
The process continues with the generation of a pre-distorted radio signal by processing the original transmit signal through the machine learning model. The nonlinear pre-distortion technique proves invaluable, especially when considering scenarios like sending radio transmissions from a cellular base station or to mobile handheld devices.
As the pre-distorted signal undergoes further processing in the transmitting radio signal stage, the resulting transmit output radio signal is transmitted to one or more radio receivers. The model’s adaptability is highlighted, catering to specific deployment scenarios like tower-mounted amplifiers, remote radio units, or systems involving interference cancellation.
A unique aspect of this innovation is the continuous improvement loop. The model refines itself by obtaining a received radio signal corresponding to the transmitted output. Through a distance metric computation, the model parameters are updated, ensuring optimal performance and adaptability in real-time scenarios.
DeepSig’s nonlinear pre-distortion machine learning model stands out as a game-changer in the field of wireless communications, offering enhanced linearity, minimized interference, and adaptability across diverse deployment scenarios. This innovation signifies a step towards more efficient and cost-effective radio signal processing, marking a significant milestone in the evolution of wireless technology.
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