Reinforcement learning radio for adaptive spectrum reception
This technology uses a machine learning system to automatically tune and optimize how a radio receiver processes incoming signals. The system observes the quality of received radio signals, learns from past performance, and adjusts hardware settings like the tuner's center frequency in real time to improve signal quality. It essentially gives a radio device the ability to teach itself how to get better reception by continuously adapting to changing conditions.
What you could build
A software-defined radio module with embedded reinforcement learning that automatically optimizes reception quality, sold to wireless infrastructure vendors or defense electronics integrators who need adaptive signal acquisition in contested or congested spectrum environments.
Who in Virginia should care
Northern Virginia and the Hampton Roads corridor host major defense contractors and intelligence community customers who operate radio systems in congested and contested spectrum, making adaptive signal processing technology directly relevant to existing procurement pipelines.
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 4, 2026
- Status
- Granted patent
- Patent number
- 12700934
Ready to talk?
the university handles licensing for this technology. Email us for an introduction patents@harbor.capital.
Sonar summaries are generated from public patent text and are not legal advice.