Machine learning for dynamic spectrum access in passive radio sensing — NSF Award to California Institute of Technology (CA, $759,
This project develops methods and tools that use artificial intelligence and machine learning (AI/ML) to help overcome the problem of radio-frequency interference (RFI) in measurements made by radio telescopes. The techniques could benefit other sensitive receivers threatened by RFI, for example weather radars. RFI is
| Award title | Machine learning for dynamic spectrum access in passive radio sensing |
|---|---|
| Award ID | 2537086 |
| Awardee | California Institute of Technology |
| City | PASADENA |
| State | CA |
| Amount obligated | $759,977 |
| Principal investigator | Vikram Ravi |
| Program | SII-Spectrum Innovation Initia |
| Start date | 10/01/2025 |
| Abstract | This project develops methods and tools that use artificial intelligence and machine learning (AI/ML) to help overcome the problem of radio-frequency interference (RFI) in measurements made by radio telescopes. The techniques could benefit other sensitive receivers threatened by RFI, for example weather radars. RFI is a growing challenge for instruments like telescopes and radars due to increasing usage of the radio spectrum by mobile wireless communications and other applications. The undesired |
| Source | NSF Awards |
$799/mo
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