Recurrent Neural Radio Anomaly Detection
This addresses the challenge of anomaly detection in radio communications for applications like spectrum monitoring, though it appears incremental as it applies an existing neural network approach to a specific domain.
The paper tackles the problem of detecting small anomalies in complex multi-user radio bands by introducing a recurrent neural network-based novelty detection method, demonstrating improved detection performance with quantified probability of detection and false alarm rates compared to baseline methods.
We introduce a powerful recurrent neural network based method for novelty detection to the application of detecting radio anomalies. This approach holds promise in significantly increasing the ability of naive anomaly detection to detect small anomalies in highly complex complexity multi-user radio bands. We demonstrate the efficacy of this approach on a number of common real over the air radio communications bands of interest and quantify detection performance in terms of probability of detection an false alarm rates across a range of interference to band power ratios and compare to baseline methods.