  Because of their highly directional transmissions, radios operating at
millimeter wave (mmWave) frequencies need to perform beam management to
establish and maintain reliable mmWave links. Existing solutions rely on
explicit coordination between the transmitter (TX) and the receiver (RX), which
reduces the airtime available for communication and complicates the network
protocol design. This paper presents DeepBeam, a framework for beam management
in mmWave networks that does not require pilot sequences from the transmitter,
nor any beam sweeping or synchronization from the receiver. This is achieved by
inferring (i) the Angle of Arrival (AoA) of the beam and (ii) the actual beam
being used by the transmitter through waveform-level deep learning on ongoing
transmissions between the TX to other receivers. This is possible because
different beam patterns introduce different "impairments" to the waveform,
which can be subsequently learned by a convolutional neural network (CNN). To
demonstrate DeepBeam's generality, we conduct an extensive experimental
campaign where we collect more than 4 TB of mmWave waveforms with (i) 4 phased
array antennas at 60.48 GHz, (ii) 2 codebooks containing 24 one-dimensional
beams and 12 two-dimensional beams; (iii) 3 receiver gains; (iv) 3 different
AoAs; (v) multiple TX and RX locations. We also collect waveform data with two
custom-designed mmWave software-defined radios with fully-digital beamforming
architectures at 58 GHz, and implement our learning models in FPGA to evaluate
latency performance. DeepBeam (i) achieves accuracy of up to 96%, 84% and 77%
with a 5-beam, 12-beam and 24-beam codebook, respectively; and (ii) reduces
latency by up to 7x with respect to the 5G NR initial beam sweep in a default
configuration and with a 12-beam codebook. Additionally, we pledge to share our
waveform dataset and the full DeepBeam code repo with the community.
