ML-driven adaptive signal processing for wireless receivers
This technology uses machine learning to automatically tune and optimize how a radio receiver or transmitter processes incoming signals in real time. Rather than relying on fixed, manually configured signal processing rules, the system continuously observes how well it is handling RF signals, compares current performance against historical data, and adjusts its internal processing parameters on the fly. Think of it as a self-calibrating radio that gets smarter over time by learning from its own performance history. The approach is general enough to apply across a range of radio hardware stages, from filtering and equalization to demodulation.
What you could build
An adaptive signal processing software layer or embedded firmware module for wireless base stations, software-defined radios, or military communications hardware that autonomously optimizes RF reception quality without human intervention; buyers would be telecom equipment OEMs, defense communications integrators, and SDR platform vendors.
Who in Virginia should care
Defense communications contractors and systems integrators in Northern Virginia and the Hampton Roads corridor — particularly those building SDR-based platforms for DoD — would have direct procurement 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, Thomas Charles Clancy III
- Granted
- August 27, 2019
- Status
- Granted patent
- Patent number
- 10396919
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.