DeepWiFi: Cognitive WiFi with Deep Learning
This addresses WiFi reliability issues for users in interference-prone environments, representing a domain-specific incremental improvement by enhancing existing WiFi with deep learning components.
The paper tackles the problem of WiFi throughput degradation due to interference and jamming by introducing DeepWiFi, a protocol that integrates deep learning for tasks like spectrum sensing and signal classification, resulting in major throughput gains compared to baseline WiFi and other jamming-resistant protocols, especially under low signal-to-interference-plus-noise-ratio conditions.
We present the DeepWiFi protocol, which hardens the baseline WiFi (IEEE 802.11ac) with deep learning and sustains high throughput by mitigating out-of-network interference. DeepWiFi is interoperable with baseline WiFi and builds upon the existing WiFi's PHY transceiver chain without changing the MAC frame format. Users run DeepWiFi for i) RF front end processing; ii) spectrum sensing and signal classification; iii) signal authentication; iv) channel selection and access; v) power control; vi) modulation and coding scheme (MCS) adaptation; and vii) routing. DeepWiFi mitigates the effects of probabilistic, sensing-based, and adaptive jammers. RF front end processing applies a deep learning-based autoencoder to extract spectrum-representative features. Then a deep neural network is trained to classify waveforms reliably as idle, WiFi, or jammer. Utilizing channel labels, users effectively access idle or jammed channels, while avoiding interference with legitimate WiFi transmissions (authenticated by machine learning-based RF fingerprinting) resulting in higher throughput. Users optimize their transmit power for low probability of intercept/detection and their MCS to maximize link rates used by backpressure algorithm for routing. Supported by embedded platform implementation, DeepWiFi provides major throughput gains compared to baseline WiFi and another jamming-resistant protocol, especially when channels are likely to be jammed and the signal-to-interference-plus-noise-ratio is low.