ML-driven adaptive signal processor for RF receivers
This technology uses machine learning to continuously tune how a radio device processes incoming signals. A radio receiver captures a signal, runs it through processing stages, and then measures how well those stages performed. Those measurements—along with historical performance data—feed into a machine learning model that adjusts the radio's internal settings to improve future signal handling. The system learns over time, so the radio gets better at dealing with interference, weak signals, or changing conditions without human intervention.
What you could build
An adaptive RF receiver chip or firmware module for wireless infrastructure (base stations, software-defined radios) that self-optimizes signal processing in real time. Telecom equipment manufacturers, defense electronics integrators, and satellite communications vendors would be the primary buyers.
Who in Virginia should care
Northern Virginia and the Hampton Roads corridor host major defense electronics primes, DoD communications programs, and satellite operators who routinely license adaptive RF signal processing technologies.
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
- —
- Granted
- January 21, 2020
- Status
- Granted patent
- Patent number
- 102807
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.