  The explosion of 5G networks and the Internet of Things will result in an
exceptionally crowded RF environment, where techniques such as spectrum sharing
and dynamic spectrum access will become essential components of the wireless
communication 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 changing
communication parameters, including frequency band, symbol modulation, coding
rate, among others. Traditional CPU-based machine learning suffers from high
latency, and requires application-specific and computationally-intensive
feature extraction/selection algorithms. In this paper, we present RFLearn, the
first system enabling spectrum knowledge extraction from unprocessed I/Q
samples by deep learning directly in the RF loop. RFLearn provides (i) a
complete hardware/software architecture where the CPU, radio transceiver and
learning/actuation circuits are tightly connected for maximum performance; and
(ii) a learning circuit design framework where the latency vs. hardware
resource consumption trade-off can explored. We implement and evaluate the
performance of RFLearn on custom software-defined radio built on a
system-on-chip (SoC) ZYNQ-7000 device mounting AD9361 radio transceivers and
VERT2450 antennas. We showcase the capabilities of RFLearn by applying it to
solving the fundamental problems of modulation and OFDM parameter recognition.
Experimental results reveal that RFLearn decreases latency and power by about
17x and 15x with respect to a software-based solution, with a comparatively low
hardware resource consumption.
