DeepWiFi: Cognitive WiFi with Deep Learning
Kemal Davaslioglu, Sohraab Soltani, Tugba Erpek, Yalin E. Sagduyu

TL;DR
DeepWiFi enhances WiFi robustness and throughput by integrating deep learning for spectrum sensing, interference mitigation, and adaptive channel management without altering the standard protocol, especially under jamming conditions.
Contribution
This work introduces DeepWiFi, a deep learning-based protocol that improves WiFi resilience and performance against interference and jamming without changing the existing MAC frame structure.
Findings
DeepWiFi achieves higher throughput than baseline WiFi and jamming-resistant protocols.
It effectively classifies spectrum states and mitigates interference using deep neural networks.
Supported by embedded implementation, it maintains high performance in challenging interference scenarios.
Abstract
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…
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Taxonomy
TopicsSpeech and Audio Processing · Ultra-Wideband Communications Technology · Indoor and Outdoor Localization Technologies
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