Self-tuning AI software layer for cellular base station radios
This technology uses machine learning to automatically tune how a cellular base station processes radio signals in real time. The base station observes the quality of signals it produces, measures key performance metrics, and feeds that information into a learning system that continuously adjusts how each stage of radio processing is configured. Over time, the system learns better policies for handling incoming signals, replacing the need for manual or static radio configuration. The result is a self-optimizing base station that improves signal quality and network performance without human intervention.
What you could build
An AI-driven software layer embedded in or layered onto cellular base station hardware that continuously self-tunes radio processing parameters; telecom equipment vendors, mobile network operators, and Open RAN (O-RAN) platform providers would be the primary buyers.
Who in Virginia should care
Virginia-based defense and intelligence contractors (e.g., SAIC, Leidos, Booz Allen) operating secure communications infrastructure, as well as commercial carriers with network operations in the Northern Virginia data corridor, would have natural interest.
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
- January 21, 2020
- Status
- Granted patent
- Patent number
- 10541765
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.