Neural network channel estimator for wireless base stations
This technology uses machine learning to clean up wireless signals that get distorted as they travel through the air. When a radio signal passes through a communication channel, it picks up interference and distortions — the system learns what those distortions look like, then automatically corrects for them at the receiver end. The neural network is trained on simulated impaired signals matched against known clean originals, so it can estimate and reverse the damage on live incoming signals. The result is a more reliable, higher-fidelity recovered signal without requiring the kind of manual engineering that traditional channel estimation methods demand.
What you could build
A software module or firmware component embedded in wireless base stations, modems, or software-defined radios that uses a trained neural network to automatically correct signal distortion in real time; telecom equipment manufacturers, defense radio vendors, and 5G/6G chipset makers would be the primary buyers.
Who in Virginia should care
Defense and intelligence contractors in Northern Virginia — including those working on tactical radios, satellite links, and electronic warfare — plus telecom infrastructure firms operating in the region 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, Kiran Karra, T. Charles Clancy
- Granted
- July 11, 2023
- Status
- Granted patent
- Patent number
- 11699086
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.