  Highly directional millimeter wave (mmWave) radios need to perform beam
management to establish and maintain reliable links. To do so, existing
solutions mostly rely on explicit coordination between the transmitter (TX) and
the receiver (RX), which significantly reduces the airtime available for
communication and further complicates the network protocol design. This paper
advances the state of the art by presenting DeepBeam, a framework for beam
management that does not require pilot sequences from the TX, nor any beam
sweeping or synchronization from the RX. 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. In this way, the RX can associate
Signal-to-Noise-Ratio (SNR) levels to beams without explicit coordination with
the TX. This is possible because different beam patterns introduce different
impairments to the waveform, which can be subsequently learned by a
convolutional neural network (CNN). We conduct an extensive experimental data
collection 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. Moreover, we collect
waveform data with two custom-designed mmWave software-defined radios with
fully-digital beamforming architectures at 58 GHz. Results show that DeepBeam
(i) achieves accuracy of up to 96%, 84% and 77% with a 5-beam, 12-beam and
24-beam codebook, respectively; (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. The waveform dataset and the full DeepBeam code repository are
publicly available.
