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Highly directional millimeter wave (mmWave) radios need to perform beammanagement to establish and maintain reliable links. To do so, existingsolutions mostly rely on explicit coordination between the transmitter (TX) andthe receiver (RX), which significantly reduces the airtime available forcommunication and further complicates the network protocol design. This paperadvances the state of the art by presenting DeepBeam, a framework for beammanagement that does not require pilot sequences from the TX, nor any beamsweeping or synchronization from the RX. This is achieved by inferring (i) theAngle of Arrival (AoA) of the beam and (ii) the actual beam being used by thetransmitter through waveform-level deep learning on ongoing transmissionsbetween the TX to other receivers. In this way, the RX can associateSignal-to-Noise-Ratio (SNR) levels to beams without explicit coordination withthe TX. This is possible because different beam patterns introduce differentimpairments to the waveform, which can be subsequently learned by aconvolutional neural network (CNN). We conduct an extensive experimental datacollection 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 24one-dimensional beams and 12 two-dimensional beams; (iii) 3 receiver gains;(iv) 3 different AoAs; (v) multiple TX and RX locations. Moreover, we collectwaveform data with two custom-designed mmWave software-defined radios withfully-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 and24-beam codebook, respectively; (ii) reduces latency by up to 7x with respectto the 5G NR initial beam sweep in a default configuration and with a 12-beamcodebook. The waveform dataset and the full DeepBeam code repository arepublicly available.

DeepBeam: Deep Waveform Learning for Coordination-Free Beam Management in mmWave Networks