Learning and deployment of adaptive wireless communications
This technology uses neural networks to make wireless radio communications smarter and more adaptive. Instead of using fixed, hand-engineered rules for encoding and decoding signals, both the transmitter and receiver are powered by machine-learning models that learn how to send and recover information more accurately over real-world radio channels. The system works by letting the transmitter receive feedback about how well the decoder reconstructed the original message, then continuously updating its neural network to improve future transmissions. The result is a communication system that can adapt on the fly to changing channel conditions, noise, and interference in ways that traditional systems cannot.
What you could build
An AI-driven wireless communication chipset or software stack that self-optimizes encoding and decoding for extreme or contested RF environments; primary buyers would be defense prime contractors building resilient tactical radios and satellite communication systems, as well as 5G/6G equipment vendors seeking performance gains at cell edges.
Who in Virginia should care
Defense primes and government contractors concentrated in Northern Virginia and the Hampton Roads corridor — particularly those working on military communications, electronic warfare, and secure tactical networks — would have direct interest.
Readiness: Lab validated
Concept — described but not yet demonstrated. Lab validated — supported by experimental results in the patent. Prototype likely — the text describes a built, working embodiment.
Readiness is inferred from the patent text, not from a lab visit.
The record
- Inventors
- Timothy James O`Shea
- Granted
- June 23, 2026
- Status
- Granted patent
- Patent number
- 12664423
Ready to talk?
Virginia Tech Intellectual Properties handles licensing for this technology.
Prosim summaries are generated from public patent text and are not legal advice.