DeepRadar
| Autor: | Aniqua Baset, Shamik Sarkar, Milind M. Buddhikot, Sneha Kumar Kasera |
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| Rok vydání: | 2021 |
| Předmět: |
Operability
Occupancy Computer science business.industry Deep learning Computation Real-time computing 020206 networking & telecommunications 02 engineering and technology Object detection 0202 electrical engineering electronic engineering information engineering Environmental sensing Capability system Artificial intelligence business Test data |
| Zdroj: | MobiCom |
| DOI: | 10.1145/3447993.3448632 |
| Popis: | We present DeepRadar, a novel deep-learning-based environmental sensing capability system for detecting radar signals and estimating their spectral occupancy. DeepRadar makes decisions in real-time and maintains continuous operability by adapting its computations based on the available computing resources. We thoroughly evaluate DeepRadar using a variety of test data at different signal-to-interference ratio (SIR) levels. Our evaluation results show that at 20 dB peak-to-average SIR, per MHz, DeepRadar detects radar signals with 99% accuracy and misses only less than 2 MHz, on average, while estimating their spectral occupancy. Our implementation of DeepRadar using a commercial-off-the-shelf software-defined radio also achieves a similarly high detection accuracy. |
| Databáze: | OpenAIRE |
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