Data-driven probabilistic modeling of wireless channels using conditional variational auto-encoders
US11929853B2 · kind B2 · utility
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Key dates
| Filing date | Oct 18, 2021 |
| Grant date | Mar 12, 2024 |
| Priority date | — |
| Expiry date | Jun 24, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N3/044
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method performed by an artificial neural network includes determining a conditional probability distribution representing a channel based on a data set of transmit and receive sequences. The method also includes determining a latent representation of the channel based on the conditional probability distribution. The method further includes performing a channel-based function based on the latent representation.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.