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The explosion of 5G networks and the Internet of Things will result in anexceptionally crowded RF environment, where techniques such as spectrum sharingand dynamic spectrum access will become essential components of the wirelesscommunication process. In this vision, wireless devices must be able to (i)learn to autonomously extract knowledge from the spectrum on-the-fly; and (ii)react in real time to the inferred spectrum knowledge by appropriately changingcommunication parameters, including frequency band, symbol modulation, codingrate, among others. Traditional CPU-based machine learning suffers from highlatency, and requires application-specific and computationally-intensivefeature extraction/selection algorithms. In this paper, we present RFLearn, thefirst system enabling spectrum knowledge extraction from unprocessed I/Qsamples by deep learning directly in the RF loop. RFLearn provides (i) acomplete hardware/software architecture where the CPU, radio transceiver andlearning/actuation circuits are tightly connected for maximum performance; and(ii) a learning circuit design framework where the latency vs. hardwareresource consumption trade-off can explored. We implement and evaluate theperformance of RFLearn on custom software-defined radio built on asystem-on-chip (SoC) ZYNQ-7000 device mounting AD9361 radio transceivers andVERT2450 antennas. We showcase the capabilities of RFLearn by applying it tosolving the fundamental problems of modulation and OFDM parameter recognition.Experimental results reveal that RFLearn decreases latency and power by about17x and 15x with respect to a software-based solution, with a comparatively lowhardware resource consumption.

Big Data Goes Small: Real-Time Spectrum-Driven Embedded Wireless Networking Through Deep Learning in the RF Loop