This technology uses AI and neural networks for real-time wireless signal classification, enhancing detection accuracy and compensating for channel distortions by manipulating the waveform before transmission.
https://nu.testtechnologypublisher.com/files/sites/mark-saulich-12.jpg
Background:
The constant evolution of networking systems necessitates advanced methods to manage signal integrity and efficiency. The integration of machine learning and AI to adapt transceiver parameters in real-time to channel conditions and traffic demands is becoming increasingly important. However, current systems, which typically use neural networks to classify signals and decode messages, face significant challenges. These neural networks are static once deployed, leading to problems such as unavailable training data and limited computational resources on devices. Consequently, they cannot adapt to new or previously unseen channel conditions, which significantly degrades classification accuracy and overall system performance.
Description:
Northeastern researchers have created a technology that streamlines wireless signal classification through the use of machine learning (ML) and artificial intelligence (AI), particularly neural networks. This technology aids in identifying key signal features, such as modulation, device origin, and channel conditions, enabling real-time adjustments to transceiver settings. Distinctively, it addresses the limitations of deploying static neural networks in devices by actively manipulating the waveform before transmission. This manipulation compensates for wireless channel distortions and augments the waveform's features, enhancing detection accuracy by neural networks at the receiver's end. By doing so, the technology overcomes current challenges related to static post-deployment neural networks, unavailable training data, and limited computational resources, ensuring more accurate and adaptable signal classification.
Benefits:
Applications:
Opportunity: