  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.
