Learning-based MIMO baseband module for real-world 5G channels
This technology uses machine learning to automatically figure out the best way to send and receive wireless signals across multi-antenna radio systems (MIMO), which are the backbone of modern 4G and 5G networks. Instead of engineers manually designing complex signal encoding and decoding rules, a neural network learns to do this by treating the transmitter, the wireless channel, and the receiver as one end-to-end system it can optimize together. The system trains by repeatedly sending data, measuring how accurately it arrives, and adjusting the network to minimize errors. The result is a communications system that can adapt to real-world wireless environments that traditional math-based designs struggle to handle.
What you could build
A software-defined baseband processing module or chip IP block that telecom equipment makers and chipset vendors could license to replace or augment conventional MIMO signal processing in 5G base stations and devices, with particular appeal to companies building next-generation Open RAN hardware.
Who in Virginia should care
Northern Virginia is home to major telecom infrastructure operators and defense contractors (e.g., Leidos, SAIC, Booz Allen) with active 5G and spectrum modernization programs who would be plausible licensees or partners.
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, Tugba Erpek
- Granted
- May 28, 2019
- Status
- Granted patent
- Patent number
- 10305553
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.