AI radio optimization module for real-time signal chain
This technology uses machine learning to automatically tune and optimize how a radio device processes wireless signals in real time. The system monitors the quality of signals being transmitted or received, compares current performance to historical data, and then adjusts physical radio components — including antennas, their orientation, and impedance — on the fly to improve outcomes. Think of it as an AI co-pilot that continuously reconfigures radio hardware to maintain the best possible connection without human intervention. The approach applies reinforcement learning, meaning the system learns from each adjustment and gets better over time.
What you could build
An embedded AI-driven radio optimization module for base stations, software-defined radios, or IoT gateways that self-tunes antenna and signal-chain parameters in real time; likely buyers include telecom equipment vendors, defense communications integrators, and private wireless network operators.
Who in Virginia should care
Northern Virginia defense contractors and the dense cluster of telecom infrastructure firms serving federal agencies would have direct interest in adaptive radio and antenna optimization technology.
Readiness: Prototype likely
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, Thomas Charles Clancy III
- Granted
- June 8, 2021
- Status
- Granted patent
- Patent number
- 11032014
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.